{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f543de9b-3858-45dd-bddf-cddddcc60da3",
   "metadata": {},
   "source": [
    "# Part 2: Differentiable Communication Systems"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c55670f1-3ee6-481d-9661-d59e61074818",
   "metadata": {},
   "source": [
    "This tutorial will guide you through Sionna, from its basic principles to the implementation of a point-to-point link with a 5G NR compliant code and a 3GPP channel model.\n",
    "You will also learn how to write custom trainable layers by implementing a state of the art neural receiver, and how to train and evaluate end-to-end communication systems.\n",
    "\n",
    "The tutorial is structured in four notebooks:\n",
    "\n",
    "- Part I: Getting started with Sionna\n",
    "\n",
    "- **Part II: Differentiable Communication Systems**\n",
    "\n",
    "- Part III: Advanced Link-level Simulations\n",
    "\n",
    "- Part IV: Toward Learned Receivers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28fa5595",
   "metadata": {},
   "source": [
    "The [official documentation](https://nvlabs.github.io/sionna/phy) provides key material on how to use Sionna and how its components are implemented."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7c4211f-beee-4142-9289-c385d0180877",
   "metadata": {},
   "source": [
    "* [Imports](#Imports)\n",
    "* [Gradient Computation Through End-to-end Systems](#Gradient-Computation-Through-End-to-end-Systems)\n",
    "* [Creating Custom Layers](#Creating-Custom-Layers)\n",
    "* [Setting up Training Loops](#Setting-up-Training-Loops)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a184ac-1f64-407f-9a24-c53d40799be2",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d84bd69e-59f2-4a42-8dd3-730c8c1d821e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:43:38.180453Z",
     "iopub.status.busy": "2025-03-08T13:43:38.179792Z",
     "iopub.status.idle": "2025-03-08T13:43:40.646650Z",
     "shell.execute_reply": "2025-03-08T13:43:40.645712Z"
    }
   },
   "outputs": [],
   "source": [
    "import os # Configure which GPU \n",
    "if os.getenv(\"CUDA_VISIBLE_DEVICES\") is None:\n",
    "    gpu_num = 0 # Use \"\" to use the CPU\n",
    "    os.environ[\"CUDA_VISIBLE_DEVICES\"] = f\"{gpu_num}\"\n",
    "\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "\n",
    "# Import Sionna\n",
    "try:\n",
    "    import sionna as sn\n",
    "    import sionna.phy\n",
    "except ImportError as e:\n",
    "    import sys\n",
    "    if 'google.colab' in sys.modules:\n",
    "       # Install Sionna in Google Colab\n",
    "       print(\"Installing Sionna and restarting the runtime. Please run the cell again.\")\n",
    "       os.system(\"pip install sionna\")\n",
    "       os.kill(os.getpid(), 5)\n",
    "    else:\n",
    "       raise e\n",
    "\n",
    "# Configure the notebook to use only a single GPU and allocate only as much memory as needed\n",
    "# For more details, see https://www.tensorflow.org/guide/gpu\n",
    "import tensorflow as tf\n",
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "    try:\n",
    "        tf.config.experimental.set_memory_growth(gpus[0], True)\n",
    "    except RuntimeError as e:\n",
    "        print(e)\n",
    "\n",
    "# Avoid warnings from TensorFlow\n",
    "tf.get_logger().setLevel('ERROR')\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# For plotting\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# For saving complex Python data structures efficiently\n",
    "import pickle\n",
    "\n",
    "# For the implementation of the neural receiver\n",
    "from tensorflow.keras import Model\n",
    "from tensorflow.keras.layers import Dense, Layer\n",
    "\n",
    "# Set seed for reproducable results\n",
    "sn.phy.config.seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dacb1e70",
   "metadata": {},
   "source": [
    "## Gradient Computation Through End-to-end Systems<a class=\"anchor\" id=\"Gradient-Computation-Through-End-to-end-Systems\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f617618",
   "metadata": {},
   "source": [
    "Let's start by setting up a simple communication system that transmit bits modulated as QAM symbols over an AWGN channel.\n",
    "\n",
    "However, compared to what we have previously done, we now make the constellation\n",
    "*trainable*. With Sionna, achieving this by assigning trainable points to a\n",
    "`Constellation` instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7f9b5bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:43:40.651046Z",
     "iopub.status.busy": "2025-03-08T13:43:40.650642Z",
     "iopub.status.idle": "2025-03-08T13:43:40.672757Z",
     "shell.execute_reply": "2025-03-08T13:43:40.672003Z"
    }
   },
   "outputs": [],
   "source": [
    "# Binary source to generate uniform i.i.d. bits\n",
    "binary_source = sn.phy.mapping.BinarySource()\n",
    "\n",
    "# 256-QAM constellation\n",
    "NUM_BITS_PER_SYMBOL = 6\n",
    "qam_constellation = sn.phy.mapping.Constellation(\"qam\", NUM_BITS_PER_SYMBOL)\n",
    "\n",
    "# Make a trainable constellation initialized with QAM points\n",
    "trainable_points = tf.Variable(tf.stack([tf.math.real(qam_constellation.points),\n",
    "                                         tf.math.imag(qam_constellation.points)], axis=0))\n",
    "\n",
    "constellation = sn.phy.mapping.Constellation(\"custom\",\n",
    "                                             num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "                                             points = tf.complex(trainable_points[0], trainable_points[1]),\n",
    "                                             normalize=True,\n",
    "                                             center=True)\n",
    "\n",
    "# Mapper and demapper\n",
    "mapper = sn.phy.mapping.Mapper(constellation=constellation)\n",
    "demapper = sn.phy.mapping.Demapper(\"app\", constellation=constellation)\n",
    "\n",
    "# AWGN channel\n",
    "awgn_channel = sn.phy.channel.AWGN()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1b8e774",
   "metadata": {},
   "source": [
    "As we have already seen, we can now easily simulate forward passes through the system we have just setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6afe9194",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:43:40.675791Z",
     "iopub.status.busy": "2025-03-08T13:43:40.675544Z",
     "iopub.status.idle": "2025-03-08T13:43:42.163306Z",
     "shell.execute_reply": "2025-03-08T13:43:42.162438Z"
    }
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 128 # How many examples are processed by Sionna in parallel\n",
    "EBN0_DB = 17.0 # Eb/N0 in dB\n",
    "\n",
    "no = sn.phy.utils.ebnodb2no(ebno_db=EBN0_DB,\n",
    "                        num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "                        coderate=1.0) # Coderate set to 1 as we do uncoded transmission here\n",
    "\n",
    "bits = binary_source([BATCH_SIZE,\n",
    "                        1200]) # Blocklength\n",
    "x = mapper(bits)\n",
    "y = awgn_channel(x, no)\n",
    "llr = demapper(y,no)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39061033",
   "metadata": {},
   "source": [
    "Just for fun, let's visualize the channel inputs and outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3538928a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:43:42.167739Z",
     "iopub.status.busy": "2025-03-08T13:43:42.167521Z",
     "iopub.status.idle": "2025-03-08T13:43:43.564172Z",
     "shell.execute_reply": "2025-03-08T13:43:43.563586Z"
    }
   },
   "outputs": [
    {
     "data": {
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iyVn5mDEq8N1ci25dSzvuL9iNV03aLAiFJU489P6+iCIIAASPA7mxmAOm7nDEptZhC1Dy+Wms+uYo+/UIzIvXtx7FuQPlGceNbi96pthQ3+YVJYwliGhDamqXib25uVnw54M/k5meLvjzbJxq8sS+KAojRp/N+V6CyYTx48fj5ZdfBgAcOHBAUYHUYjbF1HxbzAEBUcphBABaWOZpMM2eDpz39CY8w1G8RMniHGLWfp+fxqL1B6JmSZGLJndHZxyAnjSDO8tro661TGDuyKVfwMMSIFjf5sXyTT9jzfZy3HH+QEGaZKPmwJYCEUhVhmvRiWYVohHwwXnnrrH4vqIGr35dBk8sJ7/TfF9Ri0lDsrFxv/O0OToyEfplZ2UL/yECmJ6fg6l5Dl4L4s7yWs48f3JR29KO+QW7ce8vufjj9OG6TIpfWOKUTQOhBH0yEiMETSn5QNkClEpPNqKmmXss0ACqm9tRerIRI/vJI4zVtLQjLycNvzZ7Qsz6fDhe14Y+GTTSk6y8BfPs7K6553IJT19WVVXV+e9+Ob3QKEGzKRdZPXqyvs4cYoLdDJiALiXpk5HIaooPft8rQ4YKPtS1elmFSyWLc4jVuv5z889RC7EohVNHmkG+Lm9swmgw9a1eVt/aWBgtB7ZUiECqImJyigbzwpc/Yc/xekGf2X6kGut2HsNn+50R7zET/5W5o0W2iD9mE8VrQqnp8/ra1nIkWhJ0lxTf56ex9JNSVb5LDD1TbRECZqyk6bGi0NnM3bU8A9r4XscHP03jkLMx2FWMN8xv7Znii9Cycgnmo0aN6vz3nj17BH2fz+fD/v37AQA9e/ZE3759kcLxHNSESyPOdohRg/QkKwb0QNTD0q8StcJCeeLTUqTaLKhu8aBXaiL8flqRdUis1nXjfide2nyE9/cogR40g3pJ9edqdKP4dIR/tt0GUEB1s0cXPsZyQgRSlfD5aby1LXpi8lgIFUYB4NWvyxDt7EYDnD4yWqD2AvDGt+wm4XDUNJHHMhNpTX2rF460xJA0OXzygUaLQmfzR81K5pdGie91fPHRtCSb5K/NkcINl2Cen5+PrKws1NbWYuvWrWhoaAgx40ejqKgIra0B4XPi+ReApulO4YspOeqnA7M/wUSxpi5qa5NfeK2pPhXyt9lEheRqDdbqZmWFBjR2tjuGP25LS0vU98NJT7J25mBl01qrqVVmhMub3vyu87WMJH6JoYQc2MVqXQtLnBEBNmrD9NGOshpMGqqsBS8aTOpBQNh4k5tlnx0MiW8IxpFmw9KrjZ3FgUGfYW1xgs9PY9vhaty39geMeuILLNtwSPU28Flm9ST8jM/N6sxZpwYtHn7Rw8s+O4jCkkgtsxLovSKU1+cP8TPkmw80Wl/bbeZOXz6GvD5p6JESXdjMTrEir08aj1brg1/q2kKqJlEUhVtvvRUA0NbWhlWrVvG6T0NbO579+4udf18267edlYfSk6zIygi4MHhbmzAoOwV9M5JYtSiVR+XXgh3cF6rpHZCVHCKEf//9953/zs/PD7mWEcbr6uqifsfPP0dGswfDdvChKAopiQlIPy38NbR50ezuQENrO6pZDhFqUh8j8T6DkAO7kMIkDIwQqxfuL9it2rrLhtlE4Y/TztLs+xm4hFEgUIVv3lpt+0kuiECqEIUlTox7sgg3vfkdPi+p4i34aMk/Nx/GtiPVMSv++Pw0istq8PHeEyguq5GlQlAwSlSPkUptS8D3S41JrxczUTSqGt2dZRf5RpdHu46iKPTJCP3dZhOFuy8cFPWev7twkKHMVT4/3ZmMnuGBBx6AzRbIN/zEE0/gyJHoQmJDWztWrv43/rfpCwDAkLOG4+Ip00PKpTL5OA/s24sOvw+Vta0R8/TIT4fw86GDUb/LZgs8k/Z2/gJb0YaP4W4LRKwzh4z61nY0uzvQ3NyM999/HwCQl5eHnJxQrQ7T7p9//jkkA0Ew1dXVKCoqitqGxMRAuz2e0HY3tEWWwz1Wq62LAx8oBPw+hRTnEFqYRA4rntzUt3kxb+1ubNx/UpPvLyxx4rnCnzT5bqEsWn9A9r1YbYhAqgBMQIoWDuFSeO2bo7jpje8w7skiTsGrsMSJC57bgjmrduCBd/dizqoduOC5LbIJajvKalijyvUAjYCJS+lJ32Um0jcn6tpQ19JVpjIW0dJE0TQNM2VCdoot5LrzB/fAoiuGRWhKs1OsWHTFMJxvQEf/5jCBdODAgXj++ecD7zU3Y+rUqThw4ADrZ2maxptvF2DJQ4H8nRarFU+99FqIRvBkvRsXXXRR4N8nT2LVmrUR92lpbsLSR/5fzLZmnw5A+qWyIvYPO031qSr8fdlfAAR8co9Wt3QKf3fe93ucOhUw6d93330Rn7344osBAO3t7fjnP/8Z8b7X68Xvfvc7tLVFT9HECLplZV2pmBg/53Btvt63cLHFOYQUJmHWdS2seHxYsG4PXiz6WTElSDg+P42XNv2MeWt3o0rnFiuG+lYvdpTVaN0MSRAfUpkJBKRE1zronfpWb0QiZ5+fxooth1kjBeXMl1Z8tDr2RRqiZIBTcGqWOePPYE2yrSc6/DSO1wW0S7Fy5HKliQLYo/MTTBQykq2ob23H+YN74LzcLJSebERtazuykgNmeiNpRkOJbPfvf/97lJWV4aWXXsKxY8cwefJk3HjjjZg1axYGDBgAr9eLH3/8Ef9e+w6+/moLAMBqs+Hpl17DsBEjQ+7l9flx7W/n4IknnkBjYyOW/GEBysvLcP7Fk0GBwqEDe/HvVa+gynkSw/JH4ceS/ZwtHT1uPE4cq8TXRZ/jP2vXYMw553VqTe2pqeiRHRlRP2LU2Xj/36tx4nglfntzV+nQ9/+9Gtv/F6hvP2r0GMybNy/iszNnzsSAAQNQWVmJJUuWoLq6GrNnz0ZiYiIOHjyI//u//8OePXswYcIE7Nixg7Pd559/Pr766it8//33ePbZZzF9+nQ4W2j4/DRsiYnondOH87Nak2w1hxzKxRbnYNyfoilGMpItqGnyYMG7wgLq1MZPAy9u7tp7lEwNFVxty2gUH63W1OdWKkQglZlAQIq2/khywTi8F5W6ok5QsWlJ2HLjiQpvVhklfDy56j97O/wx8xvqgVj6Cq4Ia67o/A4/HeLXZzZRsqV20poUDsH8xRdfxLBhw/DnP/8ZtbW1WLt2LdaujdRuAsCgoWdhyTP/wNjzzmd9P7NHD7zxxhuYM2cOPB43Xv37M3j17890vp+YmISnXlqJrZu+iCqQ3nbv77Fp4ydo93jw5OKHQt67+ro5WLb8lYjPLPjjX/D26y9j29ebse3rzRHv5w45E/+35l3WOvZWqxVr167F9OnT0dLSguXLl2P58uWd75vNZrz44ouora2NKpDed999ePXVV1FbW4vFixdj8eLFne+dM2ES3vzPZ5yf1ZrWdh8WThmKgdl2xaOovT4/fq9zYZQNpZLGc2UlMA763z+jQUz2MqP3gBQhBKob7cK8tbtjnhbZHOSjwWX6r6jWNpqRD3L7eDKLYLjvVkOrFy3tPszI783xSf0RvhxazCYM6JHMmvKJT3R+vJFgomC3cesB5s2bh8OHD+O5557DtGnT0L9/fyQmJiIlJQWDBw/Gdb+9Ac+ueAMfFG3jFEYD32PCddddh7Uff4nJ069EZo9sWKxWOPr0xdXXzcE7n23G1JmzYrZ32IiRePujL3HFrN8gp28/WE/7ukbDYrXi5bffx5+fegGjxp6L1PR0JCYlY+iwPCx45M94d+PXyOzZmzMB/wUXXIBdu3bhlltuQZ8+fWCxWJCTk4Pf/OY32Lp1K/7f/4vtatC3b1/s3LkTd911F4YMGdLpU2oU3v3+OK4c1QcTB/cQLYzuLK+N6TbW4tG22ptYgktCy2W+l5qWUQ+IKc6hJ4iGVGaMEJAihM9LqmJfFAQfgTxaPd/PDug7UpCiQqtfSSVWahYA2CjwGWgJDSAnPQkWMxWzUhOf6Px4o29mUsxcnBkZGbjnnnvw8MMPd6ZBYqBpGj+6mmJWHrLbzGjx+JA3eiyWr/o357XLlr/CquUMZnj+KDy74o2o14RjNptx/a134fpb7+K8pqrRjUSLifWwctZZZ+Htt9/m/OzSpUuxdOnSqG0YPHgw3ngj0G5XQ5vkClRqIodrUDwpR9iQO0d0rKwERuClzYcxLCfVsCmgiIZUZsYNyITBDymSiCWQG/0UStPArsroKWmEEA+LYDgWc8D/MyUxIarwJab2u1GJpikWAls2gnDSkyyobm43hEDyS10bmt3ezij84JRYcpISRSutV6Q+v3hTjnAh1zg3wnyJBQ1gsYGj7Y03S3XOrsq6qGVA4xUKAef7WGlJ4kEAk3PhiodFMBwmSj5W+cxoUffxgtlEYUBWMuy26MK5ELgqDzGBZVrn1BSCzx+IwmfgW2pWKHZbAswmylAbtVSBcnxuFnLSE+FqcBtWAcAHuQTveBHg61q9+Ofmw3hw6plaN0Uw8b8jqEw8Chh84ZOWJB76R86FK14WQQbGXMyW75FJ3M7Algyf7X4DspJjXqdXfH4azR5fwFdPRu1fepIVwxypGJSdguyUgF9nPAgdwblU5aSxzQuFlK+KkGW3CMo5yobZROHxq/JkapH+EJObNRqMAB8PBs6XNh/WLHerFIy5yuuYeBMw+OBIs/GOdjR6/8i5AALxtQgCQJ/0RDS6vaz5HsOFDT7m5z4ZiUhP7hK+MmQuE6oGp5rcOFrdjFJnI6oa3bIJphRFnRb+jZXvmA8n6yP7iaZpNLs7BJv3nQ1tqKxthd9AEum1Y/rKElk/PT8HD04xnqYsFuG5WeUo1sII8MYZJdzQAOYX7DFc9SZispeZ7mImYVg45UwsmDyE9+LJ+NgayHIWwtWjc2RNwcIsgvet3R0zl6cRMJsoHK+LnrQ8uK49l/k53HTLlH1scBtX+PL5aVQ1ulHd7Amp7S6FeA0MY0rNpiQGtii2XLV8zPsNre341UDBTAxT8hyy3Mfnp+H16T9tnFCCc7OypcwTk6fU56eRarNE5IE1MovXHxCUilFriIZUZuLdTMKQk56IlTePxQNThgoa7Eb3sX3vh19k90Obnp+DV28ei/Rki6z31YJmEXXtg83PZ2QlY1B2CoY5UlkFDatBTffB+Py0bGZpvQSGnTvxAuw7Xod9x+tw7sQLZLkn89u4KizFMu/TNI0TBkwrlpFklsUKw6TWW/FVWeyLDcT8Swbh2z9N7hRG2VLmMXlK+WoImb666c3v4kYYBQL+pCu2RBaz0StEQ6oAjIDx6IclqG2R1xeK4XnMxWxLIA0RTQPrvcAjKFDku4K5a9JATMlziE7WrLQPqdL9Ut/qxfbD1bjwrMgKNXLcW0nUGDOeDn6LebggxWhAY9EjxQpnQ3QNrFD6oRyZQUO5jgZ+Qa6s38FGsKZYLEoGhmnVLwwJJhOvXLVc/cgE1CmBknPp/MHZkjVaWiV4V2ON6WG3dZrpo6XM41usRY2+0mq/BoA12yqwYLIwxZFWGF/doFOm5+fgpRvGKHLvMstcXGcDzGbAZAr8/zpb4HWlSE00Y+XNY7HkqhGSkjUr6UOqVr/c/q/vcd/aH7DtcLUs2lKfn8ai9ey1y+VCjb6hADS5O2JeB4gXpCgAJpmi1QFgJBUQuqig/zKpwOtKE64pFgOfwDAxaNkvQGguVaEad4ZGhdw7lJ5Lg3qmSvq8Vqn11Fp/M0/7kcfK2MKnWIsafaXFfh1MfZuXd8EarSECqYIokX6lzDIXXHu5yaTcIH/iqnxZku2Oz81Cll1+07Sa/eLz0/i8pAo3vfkdxi0rkuw4vuNojaLaUbX6hgZ4BY4wKaDE0OLxyRacEku4UkP4kqrB4xMYJhQ99AtTapZv/4Rf19DWbtj1d+LgHp1BOh/u/gVvfnMUH+7hH6wjJLWeI80mS0ClmuvvnuN1+HjvCWw78iuv66NZ5ZROQ6jVfh2OUbLbEIFUIQpLnFi24ZCs93weXYM7XEnE/G0yBa6Tm5yMJEmRjMxnP9t/EuflSq+qEYyW/VLf5sU8Ab5KbBSX1cjYolC07BsuMpKFm6mZCGu5gpr6oUuo4uqX8OuUQA6TeyAwTJ7UWFr3i9lEhRQQ4Ns/CSYqKALfq0hJ2kfwsOJzKdFiQl2zp7Os8sL392HZhkNY+F5XeeVYaw1f4eOKfAf+fv0YydpBtdeYf+84hgfe3cvbNzaaVU5JQU1Pa69RstsQH1IZ8flp7CyvRVGpC6u3Vch+f8YHhQvmvdkW4BEZFW5ZdgvqWgKLpJhIRrYoSDnRql+CWfTf/RKiGZUzGOmhb8JJSxSmIWeLsJYKY47mgnkvE8AvCj0es4kSrSkGIgsP9MtMQnlQknkxaN0vvVJDo+YZl4Roz57J7KB0toGpCQAVZY7IMZfcXj8WvLuX833n6WCdaGn2+Aofn5e4xDQxAj2uMQC/Yi1KCmp66Be+BWv0AtGQygQTpTdn1Q5FhFEg+uAWcx1ffjO2L+4v2CMqkpErClJOtOqXYOrbOrDjqDhN58RB2TK3pgs99E0wjG8gX7girOMBn58W7efIVnjgWG2rzC1Un4SwAx0fl4RAaiPlx4ee5tITn5ZyWqjqWvi7KsghlOqpXzq/6/T/YxVrUTIPtF76hU/BGr1ABFIZUEPoAsC70oic+Z/vuiAX/919kjOSEeBeHNVyrteiX9gQa3qfMLgHMhRK+aSXvmFgfAP5wCfC2uiwJYCPBZeQbqSymFywuR1wuSRYzCZVN1q9zKVowTo+Py27q1jM9uikX4JxpCfyKtYSnKZR7pGkdb8IKVijF4hAKhE1IxrXewODl2sAM++tl0n9n2w1Yf3uX6Kmroq2OKpVt17tfuFG3Cgwmyg8O3ukzG0JoEXfmCgqQlCwmE0hvoF8UDLpex3Nr1/qFJ7YQiPtlRbSteyXaC4MbLlq+2cmqSqEF3XoZZ0JwOb/qNaaG4x+1t8AS2YO78xTygcmTaMjXV7zvZb9ct3Yvti26DJDCaMAEUglo+YC8AgKwASThg9y5m+/X778Zq3tftTxjP5mWxzViuxTu1+4kGJ6n56fg5U3j4UjzSZji7TpGz9NdwoKZhOF3mmJnInuo6Fk0vfgfJpc/RJ+nVII+Z1KV2bSsl+yU2xRtedMrtqMZCtSEhPQobJG+Hm8oIt1hoHN/1GLaGq9rL8M2ak23ppzJtjW0+HHC78djXfuOg8LLh0sSzu07JcLz+xpGDN9MCSoSSJqLwCDvQWcqST8/sD7WsC2OKoZ2ad1v2QkJWDCYGnZA6bn52BqngM7ymrw/g/H8PE+eeoQa9k3TLnMRIuJl0AaHKjj9SkrcBygc6OmMDpAq5MEXkikvRqVmbToFxNFoVeqsMOYkkUBuNB6nQGiB6poFU2th35h4NsHXCVH83Kk5YENRqt+yU6RV7GhFkRDKhEtFoDB3gJ84AF8vsCg9vmADzzaCKMUApOYbXFkHMbVQst+uWNSriwn0qJSFx7+YJ9swiiD1mOGj69keKCOs6FNkWCDYA7QuSFmasYcrZYwKjTISy0hTO1+oSA8kT2fogAJJioiUEoqWs8lgDtQRckgnVho3S/R9qJwuOI+nA1ubP6RX35TvmjSLwZ1JycaUokwC4Crwa3qGHgEBaqm0IgG1+LIOIzPW7tbtbZo1S8Ds+2S76F0CTstxwzjK8lVHpQJ1AlHjTn1C3IVS+0Uiyy7VVBOVj5pkCjI029q9ouPplFZ04oBPcDbtYOJwGcbNwx9M5OQmmjBoZON8MkYPaLVXIqVao9Zc+9bu1u2cSAErfqFb1Q9oE0lK7X7pVpApgU9QTSkEuETpZffJ029BqlID7s1ZhQf4xuZkRTfZx+pmnKtyv2pCZe5uTtE03NhSxC2BPNJg5Qt0PStJ4RmHYgWgc8E0f3a5JFVGFWT+ZcMwpKZw7H8hjFYd/cEXsE6XEE6Kbb4WIPtVjOSraFWBb5R9YA2gV9qY5RE+OHExwjVGGYBYPNHefyqPKQnWTFn1Q4NWyg/WXYLihdfBiuPDXVqngPHa9vw1EZ105GogVyJh7vDIsllblY6UEfPiDHBB4QwRBQLsJhN6JMRSCyfbDXjl7o2w6WCiqVJZyM9yYq0REtIkQC7zQyKokDTtCIlRNUgJz0Rf7h8mChXIMYffWd5LU41udErNbBGFZW6FC1SogapiRY8dmUeMu3WkN/Gt5+MUkZTDEZLhB8OEUhlgmsBMJsofLz3hNbNkw1myj997UhewqjSVZq0RIiZKBZGXyTNJiqq8BPNV1KNQB09wvTJ119/jUsvvRQA8Pjjj+Oxxx6L+dloQljo+x1wNbrR2s4/tZTWiBkPTAR+OC0en+GEcgap64rZRGFiWKAls0+9ta1c9XylcuFqdOP+gkC1qllj+gr+vFG1h3wxUiL8cIjJXkaYBWDWmL6YOLhH56CIpwkgxDSiVsEArRDSF7Ew+hiJFdUZLSG+FtHSekBIkQA2wtMghd+Loij4aFp3wuj3xd9idP9MjO6fie+Lv414X87xYNTDTkayBak2Cz7eewLFZTWyCtVmE4XbJ+Uiyy4sDZueoBG9WlU0xudmISNJmUIkWpKZnGC4RPjhEA2pCozPzYIjzQZXozFNRw9eNgS5PVMEmUbi2SdywaWDMWlIT0FmolhoFRwnBxazCXarGdkpNtS3tofkhww2I3OhZqCOHuDTJ3JgRN9coVkHYmHUw059qxc3vfld59+xgpmEYjZReHJWPuYXqBdwKjdMQZZwLXAszCYKd0waiOWbDivUMm2wJRhfnDPmbDUYZhOFpVeP0LoZonnvh19w5ag+IVrfWMSrT2ROeiIWTj1LUF/wITg4zmj4aRpHq1tQ3exBh59GgsmE7BQbBmWn8EqIH++BOsGILRIQDZqm0ezuQH1rO5rdHZ1BQUb0zZWqNQ7HbjMb1nwZjKvBjfvW7kZhiXzp4GaMysG9F6mT3kwpxLo6LZg8VLFyzVpR1Sj/GFEbIpCqRGe0uQEnAVdp0GgY3SeSDQrK++ekG3B8hJvNOvx+VDd74KP9vIWLWNHSOelJrO8bDbkDbMJztx6tbsaPriY0tLXDayBztZjSst0JZoaJNVNzsXhGHlbceLYmeUvlQKyrE1Ou2ai/mw2lxoiaKLq6b926FVdddRX69OkDiqLw0UcfxfzM119/jbFjx8Jms2HIkCF46623lGyiqkzPz8Guv0zFO3edh8vzemvdHEEIFTCN7hMZTo6M/qJsMP629TxLtRoBMSl8wuuVB2sTI9+3Y1C2HWdkJSMz2RiCjM9Po8XTwfv6r7/+GhRFgaIofP311wCA999/H5dddhmye/ZE78x0XHHBOCx/6jE01NUBCESqV9a0otnd9T1LFs7H6P6ZuGLiKABAlfMknn/iz7jqonNw3tA+uGT0ECy47Xps+2oTZ1tOHD/W6fv58fvRk3pfMXEURvfPxJKF8yM+/7vrr+p87XfXX4XR/TOR1ycdGcmB0qF813wuzXAwRg5qCoeGOOVALHqk2gzpDtPDbpUUTd6ZHistfvYqpcaIWijqdNDS0oLRo0fjzjvvxOzZs2NeX15ejpkzZ2LevHl45513sHnzZvzud79DTk4Opk2bpmRTVcNsojBpaDZMJgpfllZp3RzeCBUw+fhEUlRkjV898ugVw3D7pFzsqqzDx3tPCE4zEot49bcVk8KHK1o61vvpSRY0tHnhN8CAavZ0ICVRuCbc7/fjlltuwdq1a0Nerzx6BG+t/Ce2FG7Amv9uRHavwGGXq/LRwX17sOD2G1Bb3VWRxu1uwzdbivDNliLccvf9ePixJwW3T00a2tqjpr1iMGpQUzTktj4Z1Zq1bFa+5DV4en4OUm2WEH/deMCoz1RRgfSKK67AFVdcwfv6lStXIjc3F3//+98BAMOHD8e3336L5cuXx41AyjA+NwtZditqW9q1bkpM+JZjCyZaxRDmbwPIDgCA78prsGZ7BWuOWTk0pvHqbwuoJxBQFIX+WUlRq/boB3Gb6JIlS7B9+3Zcc801+O2NN8Gc1hM11afw7r/ewDebv8SxiqN4/olH8dzLbwKIdKUAgLa2Njx83+1obmrEnfc/iAsvnQqL1YYDe3dh9Yrl+PWUC/9e9TJy+vbDTXfNk/Qrw+nlyMEHRdtwcN8ePP7wAgDAipWv4+JJE0Ou69evX9T7cFX1YjTDwdWejBrUFA25rU9GtGbde1EuZoySx1pl1KpG0TBqLXtdhWUVFxdjypQpIa9NmzYNDz74IOdnPB4PPJ6uAdXY2AgA8Hq98Hr1bf5cdtVwPPSfvVo3AzYTHfL/cB6beRb8vg74BWaPueysbLwydzSe/fxHuBq7BC5HWiIuH94bb39XKbrNavLt4VMAgODg37rmNjy4bheW3zAGU4ZLc7841dACm1m6dB7rOfLFaqJBUQENtlQlcIKJgl8OodTvA44VA80uIMUBnDERMIVGY6faEjAgKxkn69tUr8xjoij0sFvxKw8fUbvNHNInwf+mabrT9EzTdMh727dvx7Jly/Doo4+isc2LX+rbAAAXXjoF826+Dtv/twWbNn6C+tpqZPXIBgCYT6d+YmTguppqNDVasGrdhzhnwqTOe48eOw6Xz7gKc6+agirnSfzzb09i5rXXdd4n8Bu7fgPvsRF0nc1qwVnD89BY12VOHD50MPLyIoP5oo0ZZ7076nc7691IPV2VKNlqgtVsknQw8p+eC1YTLWieipmP0bJJUAgExZ3dL1XWve3sfqkYkGlDVaP+MnyYKCD4XJWZbMGSmXm4fIRDtj7ITk6I+lxjPcfwNuoBf0eHbuQfIe3QlUDqcrnQu3fo5t67d280Njaira0NSUlJEZ955pln8MQTT0S8/uWXXyI5OVmxtsrF38Zr3YIulp3Dvmi3l+/CxnLx931oWPgrLQB9VFe/XSxS+4ZBzr7geo58SUigkGml4UgCrBJdMzvcLWiUqPy1HPkcSV8/AVNzV/SoPyUHbZc8Du+QSAtMjibTngZoD/rZY1/pC+uT1tYubZ/H40FTUxMAoKmpKeS9MWPG4P777+88dHd9F4WHfj8f2/+3BR0dHfilZCdGzZjR1S4A9qCV/o7bb8c1l3UJowz9BufgmaeexJ133om21hb87+N1+P3vf9/5vj+oX7NsiPpbzacFRntC5HUVQct4a2tr5+/hiyNyGwjDH3LP2NdHp70d8FppPDzKj44O4Tldpc7HUFrwReHnMt4vQOQarVd86KjcjY0y6zL4rL/yPkdlqf3pO2z8SetWBAhew2KhK4FUDIsXL8ZDDz3U+XdjYyP69++Pyy+/HGlpxqgh/+VBF5Z9dhB1bfyDHeTEZqKx7Bw/lvxggsffpXpwpCXiiwcvUiSq3OenMe3Frbo8lQtl9W3nSnKuj9UXFAIamlincK7nKJQ+KSb8OYuCpQ0wRTncJlnMaPNyb9D9M5ORKsB/lJVDn4L67D6E642oZheSP7sP9G//BQy/ivWjDW1enDitRVQCs4lCTnoS0oJ+Y5O7A8fruBdgtj4JPjjbbDakpqaiqakJqampIe/dfPPNSE9P7/z756rmTs1frzPHdL6+73AlRrUEzNVn9k5Bk7sDbb6u8TBl9k042Uqx+tuOufRKpKano6mhAV9s/h+uvbNLIHUG/axaD/BLC+fPhO/0rVs6gJOtFM5ypHY6KgT/puTkZEHrdLBmOBr9MpKQFpT8vMndAWeDW5Sm1N8B1LVTeGG/CSeb+edIFTIfM5IsWHr1CEwZ3hubDlWxWpUWXTFMsjUmGlzfOyO/Nz7ccxJ1bepp3NT4vcG8+vURvPx1Get7wc/x1VvGR6z1zPrtknrylhGpe5KcCDlw6kogdTgcqKoKDfSpqqpCWloaq3YUCCzgNlukv4TFYoHFYowUOjPH9EdGShJuekM9x+qMZAtunzgQL20+3LlRePwUPD6q8+/FM0cg0aZM9LLl9P3vWxtIzKy2UJqRZEG9TAtsdWuHpLEWrS+YZ3HPRbl4fWs5r35inqNY2v0UaDrg4xtNCO6dlgjf6eTrsYJLROH3AV8sAtvooEADoEB9sRgYfmWE+T7QDrPsprSc9CRYzFREqU6G9GQrQEWvMx+OKcjPkYmoZ/4d/N7w4cND/s7JSOz0pUxNz+x8vbmpGX468L7JZEJ6shXpp4Uzq9WKC847B9Ut7GPfnGDBsBGj8P32b/Dzj6Uh/Rf871hjo+tCoG9mEsxB7TaF/dskwM8zgeczTTCbQ+6bnmxFWlKg1Gqzp0NQ0AczF9pFzis+8/EfN4zDpKEB94grRvXD5fl9WctQKwnX9xaVulDww0lJawpf5l8yCD3sNmSl2JBhT4TJnKBKHtn7LxuG1cXHo2Y5yUxJwoQhvSLa80NZDSrrPBDrGy4nTC17tnZqhZC9UVcC6cSJE7Fx48aQ14qKijBx4kSOT8QPcucn5IIZos/OHonp+TkYlpOKZzYcBNCl7nDIXBWECybthha17l+eOxYmE4VTTW5UN3kk1XWWIyiAqy+Cn8XZZ2Rq0ldsmCgKdlugXGW0muqSqNwONJ6McgENNJ4IXJd7YeAVmu5sixILssVMIeN0iikm7VD4745VZ14s4S5Igdytp4Xf4DdoP2dOz6ysLDS4o5ude2T3BAA01tdJai8A2BMTZM0tyqeqF1e1JyZDg91mRl1ruy6KBnQKEGHVhtjq0KtB+Pcy6eiUVhhQCORgXr/7ZIimUe4KVVwweUnZfiszaxddMYx1TdFLRDvTMiPXsldUIG1ubsaRI0c6/y4vL8fevXuRlZWFM844A4sXL8aJEyfw9ttvAwDmzZuHFStW4I9//CPuvPNObNmyBe+//z42bNigZDN1gVqRjuHC5vT8HFwytAe+KPwcf/vNKPRKt6tyGmeYnp+DqXmOkFN5TZMH/++9PYo4igdvAMxv9PlpvPFtueCyncy95DKNsPVF8LNg3n9rW7kkAVoO/DSNRrcX6UnWmKmaRNPMMy3a6evYUgHJDRO1HSvtkGJ9Ekaw8MuQnWKNIgRSigtiZhMF0+kxa5W5kAFT1StaNoVY1Z743EMN9C5AqJmOjgZOaydDNZRMhSo1arRzKQV6pyUCaOF0H9BLlgK1FElKouiK+cMPP+DSSy/t/Jvx9bztttvw1ltvwel04tixY53v5+bmYsOGDVi4cCFeeukl9OvXD2+88UbcpXxiQ41a5ktmDsftk3IjFj/m7xkjczRxcwg/lReX1SgmjAKRG0C0FFVC7yWVWJoRs4nC7ZNy8ca35ZprSk/Wu5GWaJG11GMIKTz9x1J6c6YCkpNAlSgaJ+vbWC0abGmH1CBc+I32PGpra+Dz+WA2c/tC1pzOT5qWkRnyuilonHNFweekJyE7xQq3gEAGoYRohkW6inDdQwkSE0y4dFgv7KyoC0nzp3cBQq10dI40G9wdflZzecAxJ1B9aGqeQ3HBnU0pcHa/1KiBZEL27owkSyA/c7vw4Dg2Fk4ZioHZdtXcOpRGUYH0kksuiVqpha0ixyWXXII9e/Yo2Cp9IkYoEkp2qs0QA1YpE0i0DYDrdJyTnoirR+fgk31OTlO62jBjZd5pn1OtEJP4XhADzgfS+gCNTrDPCApI6wP6jIk4eUp5bZefpnG0Okokz2kUF9Ql0N7ejp9LSzB85GjW9zs6OvDTwQMAgCFnDQ95z25P7fx3Y0M96+ctZgp1dXWoqanhbIMc/SKHW0TwPRrd3qhuUym2BNw2cQDeLP4FVU3Cckc/fe1IzBzTHz4/rbpfqBTkXIdvmzgAl49wAHTgvrUt7chKscGRlgg/TUeNnwiuPqSGG0O4UiBW2iI+ObfvnDQQU/Mcnda0neW1cDW68ddPD6JORHU+R5oNS68eodvDjFh05UPa3eESiuSqaKQX00Is5G5nstWMVbecE2KmZyOayfyP04frajOZnp+DlTePxaL1BzQtN9rh94f4bcrqQ2oyA9OfA96/FZEZGk/ff/qzaPFCFi1XepIFiRYzaltC/QvNJgo+P827BKXigrpEPvvvu5wC6ZbCzzqFzQkXXhzyXlpGRmcEful+dqVBgsmEt999N6oiIjGxa34H55AWihxuEcw9GN9SNq1rdkoiqtssuHVELu66ZBh2ltfi8xIn3i7ml3vo8hEOANr5hYpFrnX4lblnY8aoPpzvf7z3BK/76MVXkw0+MQDBTBzcA8VlNaKE0YVTzsSCyUN0fZgRiz5XzG4Mm1BU1+LB/AJurXGy1Yy2dl/UhMpy+joqjdzuC/deNAiThmbD56dRXFYTVajk2jT0uJkwY2XFliNYs60cbe38NDc97FZcN65vhNZXDJ4OP350NSkTZQ8AeVcD178NFP4pNMAprQ8w/Vkg72p0tEqrdhbe3l6ptiABm8LxujZwzy529Fyy8r2338RlM67G2PGhwaLVp6rwjyeXAAASk5Jx1XVzIj47bvz5+Lroc3z95ec4XlGO/gNzO9+zmE34peIIlixZEvX7c3K6NueyMvZUO1rApXX1eDyoPn1N8DrAVyA1KlLXYRMFrJgTXRgF+Au+eleoxIoBCEeogK1WgJeWEIFUh7AJPytNFJZ+UhoSgZiRZMEdk3KxYPIQfFHiwvyCSBOu3h3n2ZDTfSEz2YIFk4eisMTJao43+gQ3myg8MGUoFkwegu2Hq1D7Y/TUYVl2C4oXXwZrggl/nD4cO8pqcH/BblEpsMwmClUsufdk96XMuxoYNjMQTd9cFfAtHXB+Z6onKeUhe6cloleqLUSjG6x5a3Z3iNK+6rVkZc+ePZGcnIz7bpqNW+6ej/MvmQKr1YaSvbvwxorl+LUqUHzg/ocf7Yy2D+b6W+/C10Wfw+1uw13XX4l5Dy3C8BGj0NrSjB93F+O1V1agZ8+eMJvN+PXXX1nbcMYZZ6Bfv3745Zdf8MILL6Bfv34466yzOv1ae/fujdTUVNbPKg1frWssYY1CIJdmcPYSo8HHFB2NFXPG8irvyasvDaJQEaK44CtgL7h0CCYNydbcKqcGRCA1CLFOXzNG5WClib/JQO8I9elkgwLwzOyRKCp1sabzUDOCU2nMJgoTBvXAxh9PJ9IHez7Tp68dCWuCqfMzJhMlWz7WcGT1pTSZO1M7hcMnFVB4f/DV4orRdHKlHdIDycnJ+OCDD3DFFVdg1T//gVX//EfENXPvvBe33nM/6+cnXXIZbrrrXrzz5muocp7EE4/8v5D3zzjjDHzyySe44orIClrBPProo5g/fz7Ky8sxa9askPfWrFmD22+/XdgPU5lYwhoQSBPUXr5Lg9bJRyxTtN9P4y8fl6A2KK+t0IM+n740kkKFL3wF8YVTz4y7384FEUgNRKzTl1CTgd7h69NZUd2CdTuPwdXY5Y/GLIpT8xy44LktrBNe7QhOtVh+wxj8dcNPvA4mYvyyLGYTsuxWVu1oMGr5UvJJ43NGVjLMJpNgP1cxms5YaYe05pxzzsHu3bvxwgsvYMOGDThx4gTsdjvOPfdc/P73v8dFky9Ho9uL+lZviEDOCPH/XvUqLrvwArzxxuso2b8PXq8XZ5xxBq699lo8/PDD6NEjtobovvvuQ+/evfHaa69h7969qK2tRUeHNpXqxBJLWLvsrGxZygprTax9ZVp+juQ9R6gPZjzQXQXxaBCBNM7Qo6+jFPj6dC6YPJR1USwuq4mqSVU7glMNpgzvzbvSC1+zUXiFogaeWlW1fCnlSAXEBh/tq9TvCs9GEpxSKVamkmD4XgcA/fv3x0svvYSXXnqJ9f2UxATkpCdyBqvdcdvNuOO2mznvX1FREbMNs2fPxuzZs3m3WY9EE9ZiRWcbiWj7ilx7TrwpVPjQHQXxaBCBlBAXcC2KfDWAeo7gFAPfTYJP4EIg0tgaovXjqzlU05dSiQpJfLSv2Sk2pCVa5MsuoBPUSu5vdOJNCaAl3bEvu6MgzgVZbQhxTbxEcCoFnwAyNhO0lBKOSqKEEKWU9pVAIBCA7imIs6HPUFACQSYYDSDXWZNCwN/UCBGcSsGYjRzpoUJ5gsnEWROd0RxGQ+++lEJIT7JimCMVg7JTcEZWMgZlp2CYI5UIowQCgSATRENKiGuI4zg/gs1GtY3N6GluwqCediRFEbi6m+awu5qwFSt8QCAQCEF0v9WV0O3obo7jO8trUd3aIdgXiTEbud12lJe38hI6lPDbJOiHhrb2bnPgIGiP0cqrEuSFCKSEbkF3cBzfdKgKAHDnv76Hxxf4XWok/++umkMj8dZbb+Gtt94S9JmGtnbWYC7ZCx8QCEDcFi8h8If4kBK6DYwGcNaYvpgYo6690SgscWLhe3sjXmeS/xeWONVvFMGw0DSNk/XRM0+cqHPDLyDVFIHARWGJE/et3R2Roo+sX90LIpASCAbH56fxxKelnMn/gUDyf5+fCA8EfrR4fDFzr3b4/fjR2YiGtnaVWkWIR8j6RWAgAmk3wOenUVxWg4/3nkBxWQ2Z2HHGzvJa3sn/CQQ+8C1o0OGnUVnTSoRSgmjI+kVgII5fcQ7xy4l/umvyfwJ/hEbKJwh0ZzlZ70ZaoiUugtnC+8pM3BIUJZ7XLxKkJQwikMYxjF9O+HLK+OW8evNYIpTGAXIk/w9ZOJPNSKeFlaIk6BehkfLM9ULw+vxo8fgMH9zG1ldmvxeedh/nZ4jQIY14LV5ClEHCMfbqQeAkll8OhYBfztQ8h8otE0f4oj9uQCZ2VdZ1m00g2qbHJP+va27j/LwjzcaZ/D984cxMNOH5y3sjq9WNpKQk+X8MQTWERspzXc8HvmZ+PULTNE41eVDVGCmIezs6cKrJg18PV2PayH4AuuZjUakLH+09idqWLpcFR1oi5ow/AwOzbJ3XWtT5GaogtwAeq3wxhUCKPiMVL4mlDHp57lhk2q3dZv/iCxFIDUqsRUGIX845Z6Sp0GLxsJ00TRQQ7AobzyfPWCdtJvn/g+t2cd6jyd2BolJXRP+wLZx1bj9ONLajR3U9kpLtcZPah81sDSDktWSbGa1xklOVT6R8sKmdz/XRSDAZMySBTSsajM/dgvJaD97+5idMGdEXRaWuiPkYjKvRjeWbfobNTONv44FpL27F4pkj4mJtUkLrF2/FS/gEaS1Yt7vb7F9CIAKpTokmcPJZFIT55ehXIOU6aYbHZTnj1A2Br9vF9Pwc3HH+AKDjKOt9Wtp9mLd2N1YG9U+0hXPnL20Y1KMRv/yagrT+2YYVyhhYTbGn51NwkF/4hmjkJPB8IuWDTe18rufCYg4I883uDkMJ87E0wn53Mxoam/DNsTY4G9xYseUwXtx0mHXOcFHVGB9rE9da5Gxwh6wtYjSo8VS8JJYyCGDfv+at3Y27Jg3ElDxHt9WYEoFUh0QTOAFwLgoBU8DZyLTbcLiqmdd3VTd5sPFAIMeb3kxL0QQmNmgAj354AG1ePxxp4swgevIHE+p2seGAC8OGR78nc73ZREVdODeVt2FwVhPOxUkco9rRIzMdZrN+BQyaptHa7oPP74fZZEKytautTW52n8gOtvuE/d3eAVRUudEnPRGpKgmlfr8f7e3tcLvdMEnQOra2tYPuiB393trWigRYeV/Pht1mxaHjNSFm+wSTCb3SrEhN1KcwT9M0fvm1BXSYqwFN+4GOdng9btQ3NuOznxqx40Rg/KzeViFIGAVCUxcxc0/PsK2BAGKuxYvWH4DfT2PZhkO8Najh3/W/Ry41vCtWUalL9Gff3FaBN7dVdFuNKRFIdUasU2iixcS5KNAA7l+3B3xjUUwUsGzDId2alvicNMOpbfF2JohnJjXfCk16c0IXmg6lqskT857M9RMH98CmKAunx0fjtV2NKKv14uYEM1qbGwW3Xy3a2n1oaPOiI0jtkGCikJ5kQaLFBFeDGz6J8Vm/ngRSExOQmmiB0jI5TdNoa2tDUlKSpAOAx+vDr82xBUy60YrqBDNaPB2ob/MK+o4EE4Ukqxm/nuyIWJcoAE4AWXYrkqxmQfdVA67+8dM0Wtp9KKlyY7fTg71VXdc0COwfBmauLv2kBGMHZIk+MCsN1xp447n9Y67F9a1ezC/YE/F6uDWHEUI3lbrw4d4TqG3p6lNmvZ01pq98P0omfH4aP5TVRN1HfH4aH+09Kfm7umvgMRFIJSKnRo2PRtDtjW5SExIYHW420JtpSWqaD9dpIT4j2YL61shFL/g36jEjgRC3C7+A3LLbjvyKcQMy8eHeE1Gv8/hobDjSipsvcWDwGWnw6zBo5ZufT2HpZ6Wc7ydbE9DazqYLFUdqYgL+MPVMXHhmL9nuGY7X68XWrVtx0UUXwWIRb7Pw+WksXbUDvzZzH1Qykiy4dFhPbPmxSpSw9ZcZw/HS1qNRv6Nnig3v3D1Bd8LX5kNVePqrQyGv0TTg9tFodPvRoUCSiX/vOIZ/7zgGQH9+g9HWwOWbDou+b7A1h02DGgyjeLnj/AG4fESOroT2aS9uRWVd1zhne347y2tDAtzEEm4B00sfKA0RSCUgt0ZNjEZQTvRmWpKa5oP5PcHCKNC16C2cciYWTB4CgNscpeXCICQdSunJBt73XfFVGQp2Hg/RTHDRw27F+EH6KLPKlmnhsQ2H4WziTskDRHtPBE0+3FNwQNEDitlsRkdHBxITEyUJpAAw77JhuG/tbgCR7ggAcKLJh4Onjou6NwXgz5/9FHMcnWhqxX5nKyYO7iHqe5QiKy0FJ6KOHWXRk987n0AcKTAaYjYNKhtrtldizfZKXQjtmw5VAQgEq3WFWbErK+TMlRpsAdPb3FEKIpCKJHZah4AvpxDNqRTfE7nQ0ySIlQ5EKss3/Yx1OysxZ/wZvE3javYJn9/fw27FuAGZcNZzp3xig+8p/uoxfQAAxTFMVUrDdvjLslt4CdVyQwN46L19OF7bhtvOHwhrgn6jy6fn5+DluWPxl49LZNHcBEMDvPtfj0nNx+dmaTaGgtGDAkBrZQgXWgvtPj+NZz//EQ8Ni3yPWZOD4xayU2yyt0GPc0cpiEAqAn5pHfYISutQWOLE6m0VcjdVNHqYBEw6kHmnNTxK4Gr08DZHbSp1qSqQRkuHwlDT0o7xT28SHR0di//8cBwf7jkR0+VBSbgOf1oKEq1eH57aeAjPfH4Id12Qi8nDekcI7HoIkCssceLRjw5EWAnURo9Jzc0mCk/OyuettVMCvSgAXA3CDrRqQkM9oT18zvr99GnNKDfBcQu9U20RLmJSUULI1StEIBWBmLQO0XwRGQFXT+hlA5k8rDenMKY2b26rwLm5Waqe1LnSoQTDLH42BeJGmj0+hJu91fSrFZppQW38NLDqm3Ks+qa887Wc9ERcPToHn+xzahogV1jiVPQwx5fMZIvukpozgofXT+PKUTn4bL9T0/YUqXzYDaawxIllGw7FvlBDnA1uLC/6CZOG9FTsYMdmhclIEuYywyewVCjz39mF534zSnO3DjXQr61Jx4jRHgb7Z/rCpFU9mUsoBDZOvWwg/y4WnmZFSdien9JMz8/B/x65FFl2fSTlijaW5UZPc4MvzgY3XttaHtFuRpAvLFFe+PH5aSz67wHFv4cPda1e/K1QPwJPYYkTFzy3BXNW7cAD7+7FZ/udsGucBWD1tgpVxkU4jPVBbncOJVjxVRnmrNqBC57bIntfMf0QPmeFZp1Qgoa2DsxbuxvLPj2I4rIa1fcfNSECqQjEag8Z88yOozUhr+vBPM5AQx9VMXx+GsVlNfj65181bUc4wWmW1GRXZZ3mvm7BMGN5edFPii6SepobUlFTkF+x5YguNlOG17aWY6PGWkiAW/BoiVKrXg2YwEk1hQ29Wx+4kPtgZ5R+eHNbBeas2oFJz27W5PCiBkQgFQETbCJWZLv/ndDJVFHdIk/DZODiM3tqbhooLHFi0rObMWfVDnxzuFrTtrChppDECOaf63QBUlJrAejHdUQuwnPHKoHPT2PNtvLYF6rMko9LNNXu6Fnw4FJWKAGzpiwv+tlw1gdA/oOd0awwrkYP5qlkaVEb4kMqAj7BJtGob/Ni3trduHPSQKQnWSXleJObi4ZmK3JfvgEeevF7i4ZSQlJ4H9W1eKLm7NMTSvmV8sk0kJhggrtDfzlSo6HkoWZnea2utKMMNS3tmgbvGEHwuPvtH/CP60crphRg85M0InIGgxnVCvPQ+/uQmmjBBJ2k5ZMDIpCKhCvYxERFBjRxoaeoeiBgNrpl4kDZ78s3X6vPT2PRen34vXGhlH+t0TcKpfK18sm0YDRhFFBW8xsrKlhLwjd/NTMRfHlQ/xql1nafYgGDXNkqjIxUYdLnp1GtQCCSGrS2+3DTG9/pIlerXBCBVALT83NCylJmp9jwfXktXtysH42nEGgAW36sknVgC6mAtKOsRvP0NLG48dz++Gz/SVk3z3jZKJRKYTM1zyF7KhWtoAA4ZDjURBPkaqNUTdKaw1VNKC6rwfjcLBSVulQr1btxvxNvba+U9Z5KoUSaIz27K0hBysHO6EoABqYi4Z2TBmJqnkNX1a2EQgRSiZhNFCYO7oHCEice/s8+Qw9uCsCfPyxBW7sPjvQkyQM7Vr7WcI1a8VH9+YsGk2w1h7hXyLF5xuNGIbcJbGd5bdwIo4D0oMFYFocsu1ViS5VjxVdlWPFVGecBQ07Xj+Ca6W/qzBoVC8af1ERRsmiPjeCuIBQp6cTiRQkAdLkMrt5WgdXbKgytMSUCqQzEy+CmEfDzWvj+PgDSBa5Yi2C4Rq3sV/0Ed7HR2i49H2e4ZqvD54+7jaK6yQOfn5btlG5UH69wMpIteGb2SEkbBR+LgyM9SVpDVYDrgCGX60c8aL/uf2d3iC+wlPU4XuZQMHWtXhSVugT3RzwqAYJRM0+03JAoe4nE8+CWml5jE89SqKea3CgsceLzEu1LpwpBaLRneP7DOat24NbVO5VtpAYs23BI1qj7eIm0r5Oo5eVTIe6JT0sxbkCm4ITeekJqJgKu1E5GIzwwTcp6HC9zKBixqbL4aouvOV022WiomV5ObohAKpF4NIUwSBnYPj+ND/ee4HVtdopNd5Wq+MJ38+TaJI21XPBHzlyB4wZkwq5EGSqVkZprkq/FYVdlHe6YlCuukTpCTDnLeFYQSFmPx+dmISPZuIcUNsQeXPhqiz0d2uamlYIa6eWUgAikEolHU0gwYgf2zvJaXonce9itAA3DC/XRxkE8b5JcyHVKLyxx4qK/fYUWj3E3BwapmwTfteZUkxv3XDRI1HfoiWUbDgk+0MSzggAQP4aKSl1x4YfNhtA9mK+2+POSKjHN0RVGk0+IQCoRvoN7yczhuGvSQGUboyBCBzbf62eN6YPqFv1GBfMlWnGDeN8kuZDL9KrnNEZiELtJ8F1rvvn5V5zzVJGo79ATdS3tgrXsRtuAw+GrxRTyO5kDcbwi1B1h3IBMXQf+yUm23aZ1EwRBBFKJ8KnalJFkwbCcNJgM3NtCJz3f66fmOeLCv2ndzmOcmkCjb5JSEfP741mrLHa881lrKAAf7D4RNxplQJiW3chryfDeKXh5zlhe1wr5nfF8IHak2QRF2heWOHHx81+htqVdwVbpCINlfyJR9hLhU7Wpvs2Lm974TtbvfR5zMdsCUBRA08B6L/AICmT9DgYxyeD5VNjJSU/EuAGZ+L6iFulJCWho65DcVjX7JRhXo4cz/6ZeNkmt+kbM71dzE1WzX0wUUCfSIsBnrZFTgNdqvAQjNLft+NwsZCRZFK9UpUTfHKtrxbkx1k0xeWzVPBCrPWbGDcjknYlBy2w4Ws2lah3nJGbDwDo7/cBUbXKkqyN4lFnm4jobYDYDJlPg/9fZAq8rwdWjcwSlX2FSG12R7+hM4xIMdfq/q0fn4OLnv8JNb3wnizCqdr+Ew7Xw89FsKY0WfUNBfGUrtTZRtfvFTwP3F+wRHezFtdbInQdb67kUDt/xYDZRigd0KdU3Le1+/Gt7OZbMHA6Afd0EhOexrahuldQuvmgxZjYccPGaS1paXLScS3pRhvCFCKQyMT0/B9/+aTLeues8RVOulFnmcpr+TSZlBvkn+5y8TWbBqY2Y0qhU2NrpSE/EPRfl4vWt5bJpwbTol3C4Jj+j2QK0saBo0TdSk8CrsZBqOWakBHtNz8/B/x65FEtmDsetEwfglgln8C5XzAc9zKVwhIyHBZOHKBZRrnTfPLXxRzz6UQnuuSg34tDhSE8UnFvS56exbucxSW3ig97nklZuC1r2Sw+7VZEy10pCBFIZMZsomEyUYuai59E1uMOFPOZvkylwnZw4G9x4a1s5Pt57AsVlNZyTnyu1EXP5XZMGYt3dE/C/Ry7FJ/ucsp1WteqXzu9AbE2gWpqtcLTqm95pNkmJmZXWKms5ZuQI9rr4+a+wbMMhvF1ciX/vkE/g0HouhSNGy242UXh29kjZ26JW39S3evHa1nIsmZmHdXdPwEs3jsG6uyfg2z9NFjyfdpbXKh4UqPWY4TOXtPDj17pfRvdPN1wJUSKQyoySA5/xQQkf3AzMe7MVUA4s23CoM5k7W9LzWCYRCsDGEhfG52ZhV2WdrKdVLfuFgY8mkNGiB28yPy67AuvunoDlN4xRJNemdn1Dwe8HistqYh5k2GC0ykqZ2PQwZsSsFUonfddDv4QjRss+PT8HK28eixwZ3ajU7pu/fnYQ43OzMGtMX0wc3EOUcKGGIKaHMRPrd6rlthCM1v2y5cdfZStOohYkqElmlDQ1cg1ssdeJha00mZAyoXIvklr2iyPNhqVXj+CtuTCbqIjgDObvJIsJ89bulrV9WvWNq9GN+QWhv0Vo6cPp+Tm4c9LATtcPOdHDXBK6VqjhB6eHfgnmwSlnitayT8/PwdQ8B3aW12LNtnJ8WSotr6TafRMtUJIvari+6GHMRPudhSVOvLjpZ+W+nAOt+0WOErxqQzSkMsNEeSoBzXMn4nud6Hac/n+w746QpN1yL5Ja9cuZvezY+kfhZjQuGAFMTvQyZgBx1Zum5jkUaYuW/SI22EsNPzg9jRcAGJidLOnzzAHwzN6pktuiRd9IPbyPz81SPOemnueSlsFMWs8lI1ZrIgKpzCgZ5bneGxi8XAOYeW+9CgU5wgc7XyGzV2pip3+gXGjVLz+fasH4pzfJahaRWwDT25gBhAX0yD1WGLTqFynBXmqYX/U0XgD5NHxStIwMWvRNkUStrtlE4YmrR8jUGna0HDM0os8lLXOw6mUuGSkPNhFIFWDB5CFItsrvD/gICuD3B/4dPsiZv/1+dXMFMoM9VhBK8EmW8Q+Uy4igZb/Ut3plq9kOBPrRLuPY0duYEXpqN5soLJmZJ3s7tOoXMZHSDGqYX/UyXqSkDGNjwqAekueVFn3z2X4nNu4/KfrzhSVOPL3xkGztYUPLMZORbIl6iNdSGNPLXDJS6icikCqA2UThXoVqSQ/2dg3ycPz+wPtqwgz2aKmN2LRC0/NzcM9F8mmStewXGtJrtgNd+VuH9rLL07DT6G3MALE3Cp+f7gyIcja0KdIGNfvFbjPjnbvOExUpzaBWPlu9jBexKcPYMJsoXHpWL8n30aJv/vJxiai1RekAuGC0GjP1rd6oh1uthTEt55Lchzo1IAKpQiyYPFRWTVcwg70F+MAD+HyBQe3zAR941BUs2AY7V2ojNq1QYYkTr28tl7VNWvaLVF+d4Pyte39plLFlAfQwZoKJFYTA9MUD7+7Fsg3KaXjU6pcWjw8mEyVJwFIzn62W4yVHghaZC5+fxrayalnupXbf1LZ4sWLLYUGf0cJ3UqsxE+1wy8dyl5FsUXQ+adEvUvNAawWJslcIs4nCPRcNxnKFovseQQEeUcmPK5xogz04spUJYGLM9AxKLpZa9otY85BaJe207BuGWKUPtSjvp1a/yGE+ZA59T3xaqrjmS+3xcvGZPTHv4sER64Uc7CyvRV2rfD9G7b5ZvukwznKk8hbStfKd1GKNiXa4jVZulxlhz84eiT3H6rDqm3JZC0wEo3a/OARmNNELRCBVkAWTh2DN9nLUy7gQ6oFYg50ttVEwWjqaK0l2ik3wZ7SMAlWbWKf2eO8LucyH4Ye+d3ZUYmdFnSz31pJ5Fw+WJfgoGMYN5nOD5WNkQ0gKHyMFsogl+HDLPGc2JQjXIY7ZxwDg9a3lcbHuZCRZ8PJNYzFhkLi8tVpDBFIFYSqGyJ1bUivunDQQU/MckjUYcbtYiljR4lU4B4Bkqxmt7b7Ov2MdZOK5L+T05QrffH8/eShuWb1TlntrQSytuRh8fhorthzBmm3lilXOUxvGLYiP0K6176TSBB9ui0pdEcJmeM5jLssdAFzw3Ja4EEYB4NnfjMSkIdlaN0M0RCBVGKZiiBomNqUwUcCKOWMxY5Q86v9su3BNohHY/GMVJg0VthjErXAOoLXdh4VTzsTA7GRW141w4rUvKMjny1VY4ozU9KTZYLea0RIk/BsJGsCSmcNl0+gUljixaP2BuLNMAfznCOM76Wpwx42wFUx6sqWzPCybiw9b8RY2y11xWY1h9+V4hAQ1qUBwycjlN4zB+IGZWjdJEP93wxhk2q2iSkCyYjxLAi8+3ntScN/Euybj3e+P4cpRfUJKHwZH0AePJyP3BQXgrgsGIMseWhRDzgAdrqjpqkaPYYVRhmUbDsmSOq2wxIl5a3fHpTAK8J8jagbAaUGSxYzJw3pzuvjwzXkcT4dgpjKT5P1ZQ4iGVCWCT2eOtETMWbVD4xbFJic9EVePzsFTn/8Y1RwilOpmj1xN1BU1Le2CS/3FuyYj3MzIpuFjxtPUPEfUvqAAZNotqG3Rn7BBA5gyPAePzhgRNaBPLNH8a+Nh3LBptITC9FE8Isatgct3Mic9EVeOysF/d59AbUu7Aq1VHmeDG/8uruBdrpprTTbyITgcPr9X7xCBVAPG52bBkWaDq1F/gtnCKUMxMNuOXqmJqGtpx/0F/MwhQoinRSAcoSfuaFGg8QLTJ1wR9MHjKVZE7JOz8vHXz0p1OXdONbljBvSJJZ79a4HAs5Zaezte+0hKCh8230mudd1oVNa28rqOT1qoeFIIGFnrS0z2GmA2UZgz/gytmxHBizeMwQNTzsSsMX0xPjcLyzZIM4dwoVaCby0QI2xz5W+NF3qlJvLS8DHCSLRctjNG9cFShUshikXJg5aRNxm+SK29Ha99JKW6F9BlnYu1rhuNAVnJvK7jkxYqHvqDwcgKH6Ih1YiB2fJW45GDKcN7d/47lrZBinkgHrWCUiOFgzUZRaUurN5WIWv7tIKJLhcynmLlsp2en4N7L8rFazIXVhCLElHi4Rh5kxGKWMEyHvtoyczhuH1SrmwBX/GgRWbm2y0TB+KNb8ujuvjwmZdT8xzISLYY3u9YjXVIaYiGVCP0vnjy3RTEbh7xpBWUqyoGo8l47KoReGXu2XGhQWb6ROh4CtbqBAdEAQFfwU/26SuvpNIVUeLZqhCO2LUxnvqIqYQnpzAKGF+LHLzWWhNMgspVc7GzvDYuhFHAeJWZwiECqUYwi6de4bspSBGsg7MP5PdNE30frZFqUmMj024ztObYRAGvzD27s0/kHE960/I8OOVMxSuixHvUNCC99na89ZESwoXeFSEMWXYLLhvWE1l2a8jr4WutkHLVXBhdSAeU2YO0gJjsNSLYbK214NE71QYg1EE8lrO3XOYBs4nC+Nws1DQbI9qTAtA7zYa/Xz8G1c0eWSOpgzH6Ihmet1bO8aS3vhmYzc+XTSpqlg1VG7k0PPHQR1l2C56+dqQiwoVRgnienJWPGaP6RK3AxMCnXHU0jCKkcyG3W4eWEIFUQ/SyeM4c6QA6joa8xqcGsFwneL1pvLhgfunSq0coXg1Db4tkFs90S1ybqZzjSW99o2ahh/DNt6K6Bcs3HTacL3aW3RqSckjO2tvBfeRqaMOyDYdQ19Kuaf9kJiWgrq2D17VLrhyhmKYreB7qmWUbDmFafg7vrBVSslsYRUjnIjvVFhfCKEBM9poTbLa+4/yBmrRhY0kV6+tymEP4wFfjlZ6YgLH902X5Tj7YbeaQv9U0i+jFH27BpYOx7u4JWHIlv8j2WaP7cPaPXONJL33TicoNCfavfWDKmVhpIF9sxiy/Y/FlWHf3BLx04xisu3sCvv3TZFnnFdNH147th6evze/8bi2gKOCJq0YgNZGf/seRpuyzZOZhRpIl9sUAbAnqiwlSsi0IRYyrR0ZSAuxWc+wLVUBvB3QpEA2pDmAWz4mDe8CaQKkePexq5BYIpZpD+MC7+ojZhN3HG2T73mhQAFJtCXj9lnMUNc1zoZdMBG3tPkwc3APFZTW8rl+zvRLnDeoRVSiVOp700jcMWhd6iNSatmLdzmNR57WWMAEpaiXvjpUgftU3yq63NA38v/f38bq2h92qSpT09PwcpCZacNMb38W8dnS/dOysqFO8TeEUlbo0HyNcJFrMePraEZrmc42HqPpwiECqMxbPyEOSxYwXNx/RuimdKJXsm4GvyUTNqiI0AFejByaKwqwxfVX73mC4FkkTBahVHe7NbRU4NzcrZhWlYGIlN5djPHH1jVwCak56IprcHWj2xDaz6kFDEd6nCyYP6Uwh9tHekyFzJ9lqRqsMpUbnXzIYJ+vb8NHek7yul1rhTQpcB6Gd5bWKC6RCmDWmj2qH3gmDevDy6/795KG4ZfVOVdoUzMd7T+LPM9WLGmfGyFvbyrFsw6Go17oaPci0WwUJsQkmCh1RFm6KChxeGOw2M/x+Gm1ef+S1p/9v9Kj6cIhAqkN+f9mZePf7X1TXcPj8NPgZceRFbxqvYLQOoIlWaQVQp68YAfPxq/Iwj4fvmVrl68L7prrJE3MjicafZwxHrzRbp7DyRYkL8wui/14pUeFKEmx1+fPMvJDx46dpXpqxWOT3SceFQ3vyEkivG9sXz103WtPNk+0gpPX8DmdqnkO17+Lr133+kGxN8nSKKcUsFbOJQnYqP5/wz0ucuCI/B/975FJ8X1GL+9/Zjfo27j6KJowCocIoALR6AofGK0fl4NvD1SH3ltPnWk8QgVSHmE0Ull6tfgT+rso6TDqzd+wLFYBL48U3mEYp9Kj9AoBXTeoFwwUnrL9z0kBeSfvV2uiD++bjvSck3atXmi1EGz5jVA7u/SV6Av4bz+0v6TvVIHz8+Py0LEEcyzaU4n+PXBrzXpnJFs2FUS70ML8ZtDjccK274QLPs7NH8jqMyo0WBwa+Y+Lt4kq8XVyJnPRE3Hhu/6jCqBiYcrq7Kuuw889TsKuyTjG3Ob1ABFKdwiwUi9YfEHQypRCIYH30imGob/MiK8WGLw+68HmJK+Zn9eYL1ys1Ea5GNxa+t1f1tujdP4etr2qbWtFRqcymwWwMU/McvARSLTZ6qd/J9vnFM/Iwul8G/vJxCevBaPmmw3j3++OG0lbIZZFwNrixq7Iu6r0oAM/MHqnbzVMPEdZam1/5+HVPz8/BypvHYuknpapa7rRYR4SOCVeDG8s3HVakLUwFu12VdapqirWCRNnrnAaBwigAPHVtPn5zTn/cdeEgXHt2X9w6cSCvz2enqJe+hovwCj1KR5wC0qp8aEl4X10+ImDuW33buVh+/Whk2a2yRRYzG0OsCHepyc2lIDb6PlabZ4zqg+//PBULp5zJ+r6rwY371u5GYYm+qkdFQ65Kaaea3Jz3yjFAsu5oEdZCxpGUeZaRbNG8n6JVRmOYnp+DbYsmY8nM4Yq3R8t1RGjUvRoHGb25ligFEUh1is9P44lPS6MO9vA1gyt9Dh8hAgDGDcgU21zFUDrFz12TBiqe1kptxudm8U53Y7eao6Z1Cd8Y+GzgWgnyUir18Gnzu98fY32dmaNPfFoKn1rRZjIQnHLupRvHYOGUM0FBWN8xB5XweymRykkpoqUjW3nzWKy8eWxEVT22tffei3IF9x8QSKukpu+oFIT4WIpF63UE0F9paz25ligJMdnrFD7J4v10oEpDdqotql8JH+d15jq9EavtUrf/KXkOPBoW9BEv/jlc/mE97FbMGtMHU/McGJ+bhaJSV2eibD4J6/n6nWmB0MwEfCO/Y81HxrSmdhCGVML9S89ypPDyTWZzaVE6G4eSxDJbh783bkAmq0/f2WdkCvbtdjV6DDVu5BaOwgOm9LCOAF1jYseRU6g+tIP35+QMzNW765jcEIFUp/BV0Wen2nilJYomRDw28yy0l+8S3ValiSUA7TlWJzh3a/BEN/JGGgu+/mFCBUw18tOKha1tjADhamhDbUs7slJscKTxbzPf+Wh001pw320qdeFNFn9hPWiwlCDaOsD2Htu1IWOvoQU4vofXdxtp3Mjld8scBvW6jgBdpa038kzesXDKmXj3+2Mh62hmsgV1IjIUxOs8iwYRSHUK31OokNMqlxDh93Vgo35S8bESTQCanp/DGnjCnLyVLn2qd/gI3GIETD0L8nwFCL4oMR/Vgk898GCCU0adm5ulS024lsTqT6b/vN40bOQpkOpx3HAhpfwol0VPr+tIMI60RByr80TN2bpg8pDOHMDB46Oo1IWH/7OfV17jzu/rhvOMCKQ6JdYpVKwqn22j9kvPka0K0QSgGaP6YFp+DutCQDZUfuhZwNQapeaj0hSWOFkrFPEd/3rWhGuBmP7kI8jobdzwIV1gbtKc9ETcPinXsGNn0RXDML9gHy8FR/g6Oj0/B23tPizkUbEr2WrGqlvPwYRB7MFl8QwRSHUK36TF3W3ARoNNoCIbKkEOjDgfC0ucrLmMmawAfAP3yEElgNj+FCLIGAGufoiF0X5nOFOG95bkO+9IT+L1PfdeNBiThmRLaqtRIQKpjtFz8IiRIBsqQQ6MNB+jZelgEm7HKvFK6EJKf0oVZPQEn+wv4WQkW/Ds7JGG+p1cSFFw8PG9zUi2YMHkIfI22kAQgVTnEA0fgaAfjDIf4zUrgFZI7U+jjJtY8Mn+wpCRbMEd5+diweQhhvud0RCr4ODje/usjotIqAERSA0A0fBFIjRQg0CQCyPMx+6SFUAt5OhPI4ybWPDthwWXDsbCqWeRNTmM6fk5uOeiXKz6pjwkDZ2JAu6+MDcutMhSIAIpwXBIDdQgEOIdrbICxOtB0chZFuSE7++bNKQnr+cer+OFi8ISJ17fWh5hsqdp4PWt5Tj7jMxuvYcRgTRO6C4TW65ADQIhntEiK0A8HxSNmmVBbuTsh3geL2zI7dcdj3s+EUjjgHib2FwTjQRqCCMeFywCP9TOChDvB0UjZllQArn6Id7HCxty+nXH257PQGrZGxxmYocPdGZiF5Y4NWqZOApLnLjguS2Ys2oHHnh3L+as2oELntuCwhIn7wn91rZyQ9UUV4Jo/UjoHkSr0S7nhh/roAgEDopGn5Nq9afekdoP3WW8hCOXX3e87fnBEA2pgYk3jWGsU/Odkwbyus+yDYew6ptyLL3a2KdFsWw6VIX5Bfu6lfaBwI7Y6G4h2vXuFNEfL9HyUpHSD91pvAQjhx9yvO354RCB1MDE08TmM9E+3HuC9/1cjW7MW7sbK7uh8PXs5z/G7YJFEI7Q6G4+5sBggfVwVTOv+8ZLRH88RMvLQax+4DrUdNcMEHL438bTns8GEUgNDN8Ju6nUxWtw+vw0fiir0eTkz2ei1bZ4kWW3oq6lnXdi5sXrDxhe+BLqC+pqdKPLoysUoy9YBGXh49sHIEJg5UO8R6ATumA71GTZLXhyVn63zVggh/9tvAvzRCA1MHwn7Id7T+DRmbEdzae9uBWVdZ7Ov9V0kuY7ga4Z0wdrtlXwvm9dqxc7jtYYthSbUs7r24782i1NjQRu+FgpFq0/gIZWr6BKPd0lAp0QgOtQU9vixfyCPbj7wtxum7FAarW3eBfmSVCTyvj8NIrLavDx3hMoLquR5Lg9PjcLWXZrzOtqW7zYWV7L+f6mQ1UAGM1aF2o6SfOdQOlJFqQnWwTdu7isRkyTNEdJ5/UVX5XFfZCTnHOtO8DHSlEvQhgFukcEOoFfadFV35TjylEBwSt8RHSH8TI9Pwff/mky1t09AS/dOAbr7p6Ab/80mZeCgTH7c/UMhYDCwqjCPNGQqkhhiRNLPzkIV2OXFtKRZsPSq0eI0naZTRSuGdMHq3loDLk0kD4/jWc//xEPDYt8T02fQz7+NRnJFizfdFjE3Y0niEhxXnekJeJYnSfmr2YLcoqHVFE+P40VWw5jzbYK1Ld5O1+XS+MfD33EhhJmPiPWa5ebneW1qG7tiKuxwgXf0qLvfX8ct58/AB/vO4nalq452l3GC+N/y6wln+0/yWt8xHv6MSKQqkRhiRPzWGrYuho9EcE3Qja8qXkOXgIplwZyZ3lthGY0GLV8DvlMNLFi5Xm5PbDtcDWKj1YDCCwEEwb10PWkleK8vuiKYZhfsC+iH9nuESzYFpW6DJ/brrDEiUXrD6C+1RvxXqwsA3zmXbzm/wPkM/MtuHQwhvZO7RYCWDQYy9Od//oeHl+gD7LsVjw5Kx8zRhl7rHDB91DT6O7Amu2VAIDUxATMPrsvzshKRlaKDelJVvj8tC7GDbMmAIE1ecKQXhHtirVucL0vdi2RavbXM0QgVQGfn8ai9QeiXrPodPCNUKFg3IBMZNktIafMYGL54+jJSTraRLvx3DOwfNPPgu+ZmGDCvf/+Aa1ef+drK746gmSrGfdeNAgLJg/lXPi01IRJeS5Thvdm7Uc2GMF2xZbDeHHTYUOniuLyXWOIplnmsznEazJvZpy7GtoEBw2yYTGbMGtMX9napwRihQi+FJY4sfC9vXhufOjrtS3tmF+wG/f+kovFM/Lk+jmqEN4n4wZkYldlHVwNbahtaUdGshV7j9cJvm+TuwP/Kq4MeU0PhzxmTahtbsPfxgcOFlkpSRFrQrR1g+v9q0fnsJYQ5buWxGv6MSKQqsCOshpWjU0w9a1e/N/mw/i/zfyEgoBZ8gjWbCsPMUuGQwO48dz+nO/rzUmaa6J9tv+kqPu5O/ysr7e2+7B802Gs2V6BZ2ePjJj8WmvCxDwXxkdy4wEneqXb8b9HLsX/bT6MFV8diXmfNdsqDJ0qio/vGsCuWea0XgTNu6l5jrjM/8c2ztmIpW0PZt3OY1EPeloTbW5PzXOwrqtZdguuHdMXU/IcMTd+PmPxta3lGN0vAzNG9ZHjJykOW5+ZKEApt2ytD3nBh0+bmb1dAKIeUO+5KJdV6HQ2uPHa1nLW7xWylsRj+jEikKpAwFQcmze+OcprwysqdXGaJdlYvukw3v3+OKswNT43C460RAAtrJ/VIuKRbaIpJRDXt3px39rdeHnu2ci02+BqdGPb4V/xwe7InKdqLpJCc9YVljjxzIaDeGgY8Mf/7ofHRyEnPTHqYSSYWIcaZ4MbO8pqYDqdR1BvJ3K+vmsMjGY5mvUiuGpMqs1iyPx/bFqt7ytqUVxWg7Jfm/B5SRWv+2QkJ8DrA5o9HTGvdTV6sLzoZ0wakq2rMQJE13LPW7sbyVYzWtt9EZ+rbfHizW0VeHNbBXLSE7FkZh4y7daQuQAExuG3h3+Fs8EdIsiw8ZePSzAtP0dX/RMMM3Y2lbrwJotbmJIxgloe8vhUklr6yUEAVNRr2IRRPuh1LVEDIpCqAr/J1MKyEDIEm1bFBPZwCVNmE4VFVwxDe/kuXUc8MgKa0NyHfKABLFi3J+YCq+YiKcR5ndlkrebQH+BscGP5psOcm6xQ7i/YrUiQkBwIdSnJtttQXFaDt4srYh7snA1urN3BrtGQ2g4lYdNqURRA89wlg6+ta40tiAaz4qsjWPHVEaQnWXDnpFwsmDxE8zWEj6DBZ544G9yYXxCqUbdbzaAooNnDf54x2U/0KHTw1ZwriVaCGZ/DbXBgMhdS5fW3tpXr7kCnNCTtkwrIOZn4BDCxEa1G8JThvQEAvdP0V6OZSd3zyd4TmKTgosT3tB+8SCoNn5rRfMyDcgijQKQWVU+1k4Vo0O02M+YX7MacVTvweYmL12c+P3iK13UV1a2826EkXCnD+AqjQq/loqHNi+Wbfsa4J4s0HydCtehCaGn3CRJGGfR0gGHgGjtaoXYf6eWZfFFahRGPFWLjfu3XV7UgGlIVmDCoBzKSLbxN7NFoiGJajUWsE+cXD16EPb806cYkq4dTOhdqLVrhPrXZdhtAAdXNHhSX1cDvpzXrH734Tvr8NPw0jYwkS1TXA4YWjw+APEJ6OC9u+hlnOVI0P8Tx8adVk/pWr+alfPUiaASjtwTmehw7aveRnp6Ju8Nv2CA4MRCBVAXMJgrPzh7JGjihBduO/Brh+wToy0l6435nhFlMT6i5aDHPpbDEiYc/2BcigCZbYziqKYzW/k56PLQ88WkpJg/rjV2Vdbosw6slWh5e9CRoAEBGUoLuEpjraexoVbFJSfcwsby2tRzVze24YEg2HOlJmiuLlIIIpCoxPT8HK28eKygYSSlWfFXW+e+c9EQ8NvMsDVsTycb9J7Fg3R6tm8GKVoskVzCGXOZ4LqwJJrRzZCoIRgvtU6w0T1rACOgTntmM2pb2ztf1WIZXC7Q8vOhN0Ghwd+CLEqeuIu31Nna0iF9g/Pf1okBi+O/uE/jv6WDb1EQznrlmJK7UeXo1oRAfUpXRWhgNx9XgxsL39ka8rnbZReb7ln16EPMLYgcYaQUN9RfJ9g4/Hv3wgCaCFx9hFFBf+6RH02IwwcIooM8yvFqhldDDCBp6gaaB+QV7NPetDUZPY2dKXq+IA5xa+9L0/BwsnHKmIveWgya3Dwve3Yu73/5e66bICtGQqgSf5PhaEDydfX4aFgTM5X/5uEQ1DY8eza56obDEiUc/LOEsfKAHsuwW1TXGejIt8kFOf9tYSdtjpQzTGi2Fnun5Obhr0kDWNEZaobUPdjDjc7OQZbdGHKi0oKj0FJ74pASXj8jB+NwsRSvJsc2pBZOHYPW3R+H26nftLSo9hac2lOLPM/Vz0JICEUhVgk9yfK1gNq1dlXXYeqSGNWmvU6EcnHo0u8ZCrQ1E7360DH0zklTfTPVmWuSDHP62fAo2REsZpjU97FbN/SbTkiyafn84zgY33tpWjuxUm+bBpGYThTH907Hlx181+f5w1myvxJrtlZxBwXLkho5WTanB3REzn6zWvPltOR6ZNgzWBOMbvIlAqhJ8k+NryaZDLqwp/oXzfRryCmN6N7tyoYYfnJ79aMMpOdmI9g5/yIKodNlVvaRXEoNYYTpWUveFU4ZiYLYdvVITMTXPgVd14rMezKwxfRQTtviMuY37T4rK46w0yzYc6vy3mv7GPj+NHUdrUFxWA4CG2WTSjTAaDNcYlmp54JpT0aop6Q0/Dfy7uAJ3XThI66ZIRhWB9OWXX8bzzz8Pl8uF0aNH45///CfGjx/Peu1bb72FO+64I+Q1m80Gt9t4GpFQtDfHxKJg53HEaqcUYSx8w/DT2qUskoqSGrrCEifmFxhDGAUCvnCPrt+PF64fA0C5squdNdcb3XhruzE2CzbEmKz5JHUPFrQC1YSGIzHBDEA/AunUPIci9+Uz5jbud+J+Axzy1KoIV1ji1N2BRQxiLQ9GVYiwUVHDXmnRaCgukL733nt46KGHsHLlSpx33nl48cUXMW3aNPz000/o1asX62fS0tLw008/df5NUfoX5mIxcXAPXjXFjYAYYYxtw8jQmelMCEr5wTGLpNH4YPcJTMkLFFiIVt9Z7CYbD37GUjI0CPWZdTW4dXmoqWlyo7ishrXkpqvRjdpmD7LsVkGpbaJpjoPrjhvB/QVQx9+4sMSpuyhyqQjdl4zmh94dUFwg/cc//oG77767U+u5cuVKbNiwAatXr8aiRYtYP0NRFBwOZU7SWjFhUA/Yreao5UGNglBhjGvD4JPEXG8onfbJyItkrPrOYjdZI/oZhyO1DK/QzVavffX7d/eGtC0jOXAoZdPS8dGqx9IcU+gal0ZCSX/jJTOH46+fHYrySWMidF8yoh86F2P6Z2rdBFlQVCBtb2/Hrl27sHjx4s7XTCYTpkyZguLiYs7PNTc3Y8CAAfD7/Rg7diyefvppjBgxgvVaj8cDj6errmxjYyMAwOv1wquj6LhNh6rQ4dOng7TNRIf8PxpZyVac3S+Vd9/6/DSe2XAwos66UaEAPDbzLPh9HfArcLY41dACm8i+EvIclaCuJbDARxvjtc1t2HHkVEyB3uenA4nlG9147oufDD9+HGmJWHTFMFx2VnbMucO8H3xddnKC6HGhZ9o8gWhutjFT29yGB9ftwvIbxnSWNw5nZ3ktapvboo45PuNSCeSYj6caWuD1pgn+3KZDVVj4XkD4D/7ddc1tWPheQDOqx71ILL1TbYL2JYD/nNJ6XeVDz2SzruSdYIS0i6JpOSoWs3Py5En07dsX27dvx8SJEztf/+Mf/4j//e9/+O677yI+U1xcjMOHD2PUqFFoaGjACy+8gK1bt+LgwYPo169fxPVLly7FE088EfF6QUEBkpOT5f1BBAKBQCAQCARetLa2Yu7cuWhoaEBaWvTDle6i7CdOnBgivJ5//vkYPnw4XnvtNSxbtizi+sWLF+Ohhx7q/LuxsRH9+/fH5ZdfHvPHq8E/vvwRa4oroZzYLx2bicayc/xY8oMJHj+3aevO8wfgocuHCbr3xgNO/PG/+6U2URc8cvlZ6JuZhGc//xGuxi5zD6P94tLi8GXToSo8s/EQqpo8sS9mge9z1JrVt53LqSEN1uzEE5lJFnz9yKUAAunVqps9yE6xYdyAzAgTvtfrRVFREaZOnQqLpcvPmukbQL8meaXgGjM7y2tx57/0mRxc6nx0pCXiiwcvEuzioec+URqhazHXnGJ6/I7zB+DDXcexeLRX1+vqpME9cNcFg6KuK1rBWK35oKhAmp2dDbPZjKqqqpDXq6qqePuIWiwWnH322ThyhD0gyGazwWazsX4ueDHXgmc2luK1b47BKP5LHj8Fjy+yrVl2C56clS+qxF2vdDvrPY1Ir9RkzC9ghKWu33SszoP5BftEBewwQQebSl1Bybql9RfXc9Qaxv92wpBerIulz0/jrxt+gluHbZeKq7kDD/+3BLsq63hnHwhfw64Y1Q+UyWz44C4xVLd2sK7nDW6/Lsd6MGLn45+uGIFEm1Xw56pbO3TfJ0ohdC3mmlPMvASA1dsrAeh3XQWALT/XYsvPtZ1/O9ISsfRqdVKHxUKIHKaoQGq1WjFu3Dhs3rwZ11xzDQDA7/dj8+bNWLBgAa97+Hw+HDhwADNmzFCwpfLT3uHHqm+Mm5qGYcnM4bh9Uq7o05beq8YIYemGg7IE7DBCaFGpCx/tPamLqihKwyeox8gBXXz4bH9kiUih2Qem5+dgap4jEJXe0IZtR6rxwen61vEMW8CKz09j2QbjZaTgy6MfHYDJBMFChZ7Kf6qNmODJ4DkVnI3A56cx4ZlNirdZCVyNgdzEKxVOHSY3ipvsH3roIdx2220455xzMH78eLz44otoaWnpjLq/9dZb0bdvXzzzzDMAgL/+9a+YMGEChgwZgvr6ejz//POorKzE7373O6WbKiv/Lq5QtB7785iL2RaAogJ5INd7gUdQIOt3ZNktkoRRQP2qMUr2S7TynXyjYrVMX6TGmOHCwSNiWquoVy37RcwGajZRaGhrx9+++EnRcaRlvwSTw5HVQssDjBp9U9/qFZUqTUslgB7GjJgMBWYTFXJtcMlmOYK/tOqXxesP6KYsLR8UrzV1ww034IUXXsBjjz2GMWPGYO/evSgsLETv3gEfj2PHjsHp7NIc1NXV4e6778bw4cMxY8YMNDY2Yvv27cjLM1at1spa5SrJlFnm4jobYDYDJlPg/9fZAq/LSVu7Dyu2HMbHe0+guKwGPpES9vT8HLx681g40pU9uavVL9GIJlQx6Yu02ES16pu7Jg3Eursn4Ns/TY65qWqh2dHDmAneQPmgxjjSQ78AAWGdS6uu1QFGzb5hquMJWXsZJQCgrrOYXsYMg9SKaHJZrrTsl7pWL3YcrVH8e+RCleKnCxYsQGVlJTweD7777jucd955ne99/fXXeOuttzr/Xr58eee1LpcLGzZswNlnn61GM2VlQJYyEf5llrkwcTw1k0neQd7m9WP5psN44N29mLNqBy54bgsKSyLNjnyYnp+Db/80GevunoA7Jw2UrY0MavZLNLiEKi2rgmjZNxtLXLwTnDOaHbU2Ub2MGQY+G6ga40jNfkm0mDpzkYaTk54YVTuo1QFG7TEj5LDCwCgBeqep00d6m0uA/BXRxKCHfgmUhTUGqgik3ZFbJg6UfWN9Hl2DO7x4FfO3yRS4Tgmcp/3dhAqlPj+N4rIafLb/JPx+GhsPuGRtl9b9AgQ0EVymRUA786LWfSNkM1VTs6N1v7DBZwNVehyp3S9urx9PX5OPdXdPwPIbxmDJzOFYfv1oXlp1tQ8wWo4ZMdq+6fk5+PtvR8velnD0NpdircXRkHN+6adfjBO9QQRShbAmmPC7CwfKek/GB4Wrkirz3mwFkwsINSEVljhxwXNbMGfVDjzw7l7c9OZ3ISmT5EDrfuETsKOVeVHrvgGAolL+BxC13Dv00C+d3wX+G6jS40iLflm24RDG52bh2rP74q4LB+Hasf0wcXCPmFr1aAcY5m85yxNrOWbEaoOrW8SlkBOC3uYSoF5FtGjopV8mDspW9gtkhAikCvLnmSMwNa+XbPfjGthirxMLX62XWj6TWvdLerIlZuCBVpGvWvcNAHy896QgH7hg946XbhyDd353HtIS5S0ro4d+YaDBfwNVehxp0S9iTNIMXAcYR3oiVt48Fi/PHStHEwFoN2bEavsAddYdPc0lRww3j1jI2V966JeMZAsmiCw9qwW6S4wfb6y69Vx8uu8kHv3wAJrcHZLuxTe5vhpJ+GNpOdX0mdS6X5IsZkzNi55XV6vIV637BgBqWtoF1+QOjnrduN+JJo+8dVr10C8Md04ayHsDVXocadUvUjRTXGl7zCYKH++VLyWWVn0jVtsHBMZLRpIF9W3KlZXUw1y6a9JATMlz8PZX56KuxSNbNhg99Muzs0caJsIeIBpSVbhqdB/8v8lDJN9nvTcweLkGMPPeehVK2i777GBUX1I1fSa17hc+Gh6tIl+17hsGKRGv8wt2y75o66VfAMQ8zASj9DjSql+kaqaYA8ysMX1DzP1yary06JuFU86UlEfSbKJwhwJBpMFoOZdyTmvCl1w1gpebRzQKS5y4v2CPbAc9rdcYIQddvUAEUpU4Xtcm+R6PoAB+f+Df4YOc+dvvVye/WW2LN2qAk5o+k3roFz6/l8u8aLeZkWyV1yTNoIe+AaRFvCqBHvpFbPCFkn62aveLlAAUPjAaZTlQu28caTYskEGRsWDyUM5MBnKg1VxaOGUor3RyfFDCoqf1GiPkoKsXiEAqESaCPFauzlaZTI6DvV2DPBy/P/C+mnAFOKntM6l1v/D9veH+kevunoD9j0/DgaXT8M7vzkN6ovxeNFr2jV4iXtnQul8A8eZYJdOoqdkvQvxnhRCc2ePGc/vLdl+1+oYCsPTqEbL0i9lE4dnZIxW1zKg9lygA735/XLb7KbXWaLXGUADGDchU5N5KQgRSCYRHkHPl6vT5aXxz+FfZvnewtwAfeACfLzCofT7gA4/6wmi0hN5qp2QBtOsXoQIXm3nRbKIwaUg2LjpTmYhILfpGTxGvXGg1ZjJ4BMLFghlHSmgX1eoXigL8Mpe0KyxxYtKzXevy8k2HZb2/0n2TmmiWPDbCmZ6fg5fnng0l3QnVnEtCi0nEQkgmEKFoscbQAF79ukyx+ysFCWoSCRNBHr6Uhtem9vlpvLWtHFVN8qbfeAQFeEQF/zY+sAkOfEqGJlBAh8y+gVr0i1wansISJz7dr9zCqHbf8CkXGg21tOxajBlbgkkWk5rSbg1K9wtNA/ML9mCliZJFACsscWLe2t0ytCw6SvbNE1flK+L7l2m3KVrOGlB/LslxaPX5aXy096QMreFGizVmzfZyLJg8xFBBTUQgFUE0f5Pg2tR+P7BsgzZ1y9WES3BgfN3Ca7ebKMBPyy+Mqo2JAlbMkUeToaRgoTZX5PfGrRNzJUe8almTW2lcjR68ta0ct0/KldRHWtZzl5MnPi2VXHPb56exaP0BGVulDcfrWuHz06xZA6SgVS5kJZHj0LqzvFa2MqF6or7VKzi7idYQgVQEsTYBxpwwv0D5k7rWxDJXB6dkKSp1YfW2CsVP6WqxYs7ZmDFKHk1GvAgWALDnWD1WzJW+gfLRshuZZRsO4Y1vyyVpkeNFyGDMr1I2zx1Ha1DfqhOzkQRe31qGgu8qUdXUJSTlSLQ2ANrlQlYCCgELjBzuKvEyh9gw2m8jPqQiMNpDVhI+5mqzicL43Cx8XqKcOVpNsuwWrLx5LGaM6iPbPeNpTLkaPVixRR6/PbUqN2mFS2Q5XoZ4EjKkzgEj1eyORku7P0QYBaSPE0Abv34lkctVKp7mUDgV1S1aN0EQRCAVQTwPYL6YKOCVuWfzPrHHiwYwy27FjsVTZPfxircxtXzTYUmbZzDBEeV3TRqIVJmrNmkJo/UVUo43mHgSMqTPgXjToXchdZwA2uVClpssGQICg4mnORTOup3HRI8XLSACqQjieQDzZcUcYRrCeNEAPn1tPqwJ8k+beBxTUjbPcMwmCg1t7Vi9rQJNbnmrNmmNlIjhYCHDqMiVi9RINbvFIEdkudEtDik2M3Y8Kq9CIF4EdTZcjR7ZMhGoARFIRRDPAzgWTGUMob6TRtcAZiYHzPRKVb6IxzElZ1oWNUvRaoXYQxsjZGTZrTK3SHmkpgYLZsLgHoomgNcLUg/3wRaHy/N6ydQqdXjht6MVUQgYXVCPhpGUQUQgFQnXADZQhgVB3DpxANbdPUF0ZQymprLRuGZMH7zzu/Pww1+mKl6GLR4XRbkWw3hx+YiGlEPb9Pwc7Fh8GbLsxppjjvRE2cyvTAL4eIfPOIlVsIXJYXtm7zSlmikrjjSbogoBINI1KF4wkjKIRNlLIJ4jyMO5Ij9HUgQsU1NZ7iTVSpKTnoi/Xz8mQnOjREoWhuAxdarJjcNVzVjx1RFZ7q0Fci2GRjrlC0WuiGFrgglPXzsS953Ow6nHpeiSM7Nx78VDFJk7QGD+rLx5LJZ+chCuRnlzP2sN33FSWOKMSLXHFaU/cXAPXa8vGUkW3H/pYGSn2JCeZIXPTyuaV5MR1CcO7oFzc7PwxKelqG2WXvZbC+TMRKAWRCCVCBNB/tD7e7VuiiLIOagXTB6KNdsrDJOahc2MKGSxFwuzKAKByGE9bxhcyL0YGumULwQ5TdYAd+7fjGQLmt0d6ND4xHzvxUMUz4sYfKjbdqTakPMnHL7jhG/BFoYJgwJuDnpdk+vbvHhq44+df8u91kaDGUc7jpxC9aEdin+fnMi9rqgFMdnLAF9zYqoCdcqVRO5BrUZNZTmgKOCVuZHmIWaxD3/WcqRk4cKIwU5KLIbjc7Pi0j9QTpM1Q7Dp8aUbx2Dd3ROw89EpyE6xyfYdYpAjcIkvzKFu4dQz42Lc8BknsQq2AJGBhkZzc1ByrWXDbKIMWRNeiXVFDYwlIekUvubE68f1w+ptFbo0pbHRO82GpVePkH2zZNPg6Inpeb0wLT+0rCPf6lxSq82EY8Tk8FJLhsYrOemJWDJzODLtNsVM1gzBWnYgoGl3NWo734ymrdGSnPRE3HjuGRiYncx7nPAt2BJegIBxc3jo/X1obdd3Bgsl11oudlXWKf4dYsmyW7DkyhHolWIDKKC62aPouqI0RCCVAb7mxCl5jk6/FK6FIzPZgnMGZqKo9JScTRTF368fg0lD5E+lEu4nWVHdoivf0s8PnsIFz20JEarELvZywCXE601AXXDpYEwa0lORxXBnea1uzYrRWDJzOLJTbZpvEloKo4ESu/xzFsuJkcaNiQJumzgAl4/IETVW+CpG2K6bmudAWmKp7gVSQNm1lo3qZuV9kZOtZkF9z4yMp68dGVcHfyKQykCsmtvB/nRmE9UpjLka3ahu8qCu1QMTZcLEwT3Q0OrVTclRJSdiuAbnLEeqrrSm4T5XUhZ7OQgX4nulJmLcgEw88O4ezStgMeN74dSzFBO4jBbUxPSJ1Fr1clGrwqbKxV0X5Mpa1UwIeho3FAXQNPdBMlCKWHw/8VWMsF3H7EdGQq1nm51iQ7XC33HvRYMEKWXi1QpFBFIZiGZWZfOnCxfGGHx+GpOe3aJoWwOmoP68Br+agSSdDuRlNbi/YDfq27TVaoSbhqQs9nIRPm4KS5y6EEYB5c2xRgpq0mNAgZY5Sj/b78SiK4Zr0hd6GjcvzxkLkwmKBUUKUYyEoyfBnS9qPdtxAzLxxSFl8kMzz2RorxRe1985aSCm5jkMa5KPBRFIZYLLrCrkJKPkKTXZasaqW87BhNMCzbvfHxe1cCmJ2UTBZKIUF0anjeiN7yvqUNvSHvW6YNOQlMVeCRifVq3JsluxbFa+4if1WP2vFhEHTgpIsoSa2/SovXCkJ2n23WqaV8MRMm5y0gNWh8/2KxMwYzKxWzrkEi6EKkaC0ZPgHgu119rg/lLCTWrJzDws2xB9LQ+4vQgvSGM0iEAqI1IXGyVPqf+4fjQmDe3yBw1euILRWrvDtw8WXDoEre0dWL2tQvAiMWNkDl65aRyWF/3MKyXMqSa3pMVeCfSSKL6mpR3LNpR2brZKoZfgrojvpYG2dh8WTjlTUACK2jCCmVZjRisNXKx5SyNU6wQAP1TUya4YCA/EUUo4F6sY4SO4My4HQsmyW2Me/vmi5f60/IYx+OuGn2SbQz3sVjx1bT7Sk6wx7+mngUwDVmITCkn7JDPMYjNrTF9MHNxD0KRR4pRKgT2FEVdVIK3TRfDtg0lDsvHYVSOwUkRlo16piTCbKN4BW0yb9NRnfDf4y4b1REaSsudOtVKxcPW/lhXAmP353e+P4cpRfQTPebVgBDOtWqalBi7avF1581g8dtWIzudmNlFYenWgn+TsKznq0POFLe1XrAp70UoXM31xz4W5vNuwZObwzu/esfgy0anrwiuPabk/TRneO6RfF045E+kiUzmmJSagePFluohP0BNEQ6ojxudmwZGWKOvpnAb3ySo88e/q287FhCG9NN1QhZrGO3/D0RrMf2c3GmKY+x1pts7PijHDK2lyEwLfDX5Uv0xs+fFXRduiZioWtv4vOVEfkjxbbmJpZNWO+hWL1JRrD142FO/9cFzQZ/VSLUbIvI1WXMDb4UdLWDR0RrIFEwf14OXPrZZQIUYLy0e7OrpfJhas281ZkZArmE+odYO5z/8euRS7Kus0XWuDCe/XBZOHYMWWI1izrVyQq9mzs0fBmhDQB+ohPkEvEIFURzCn83lr5Y2yj7YIMpWmNh6C5pOdaY9Q0zij7XzuNyNj9t3Sq0eEBJeJMcMraXLjCx9huneaDet2HlPFvK2mUBbe/64G+Uv7JVlMGDcgC7nZyRjdLwMPf7A/5meMoMGILE3bhBVflcX8XLLVjN9fNhS/v2xoSLq2dTuPcZbo1Nr9Jxwh85ZLgAWAHUdrUFxWA4DGxEHZmDC4B3aW1/ISSPUuVMQS3GeMysEKnI35BXsiPhvteQs5DAXfx5pg0nytjYbZROGBKUOxYPKQoHnRihc3/cy57t57UW6IL6je4hO0hAikOoNJUrxo/YGo+fNy0hNxwzn98eJmfUXLy4FYP6hofZeRbMGzsyNztskRjKYFfITpOePPkCW/KwVg7vj+eGfn8ZjXaiGUKRGw0+b149sj1fj2CLDBzs8VwSjzLLI0bWyB9N6LBncKGaEaooCAWlTqwkd7T4b4Cup9DsWCS4CdNCQ7wt0nnoSKWIL7jFF9sNJEiVqfw4XduhYPlm04ZKi1l43INIYpEf2TZbfgyVn5Eam99BafoCVEINUhwWZo5iR+3sAeMJmpkEoMAPDeD/qLlpcDsaZxtr5jtBhcn9WLGV4oXMJ077RELJ45Ap4OvyzfQwMY1JNfWhIthDI+ATtMlaTwzY8PtS3RTXFGnmd8glkyki1YMHkI63vMRjxxcA/8eWae4eaQXAQLFVzEk1Ahds1kE3an5efE3bgR2j9GVYzIDRFIdQpjho4VeBPPJysppnETRWFo7xRJC6URCFn4GlqA43vwxYMXIdFmPS2Qy0NWik23GqBgYYCrbcyizmx+roY21La0IzPZirrWdmQkWfDU5z/GjAaOt3nGJ3vBs7NH8vptRp1DcjE9Pwf3XJSLVd+Uh/hYmijgnoty406okOt5x+u4Efq7jKoYkRMikBoccrIKpbDEqVjiab3CLHxebxo2Ht/TuYDxMSNm2a2o4ZGSxZGWqOvDD9c8CH/2XJtEcVkNr9Q0mWEpbOJhnvHtO0J0CkuceH1recRco2ng9a3lOPuMTNKXhKjEq3DOFyKQxgHkZBWgsMTJqiULLwPaXeDjm7RsVj6WbSjlXfZWz4cfKfOAr+/rkpnD4UhPirt5RtYQaTCFKtjmEPOaGlkoCAQjQwTSOKG7n6xibQhqpSXSG3w06CYTeGs+9S64iJ0HfH1fHelJcTvPuvsaIoVYhSqMkhqMQNASIpAS4gK+G8KOozUwUZQuhSmliCVECnX7iEfBJZ6ipAnqQ5Kbd198flq3B3SjQQRSQlzAd6G//53dIQmMu4ufXCwhUu+aT6UhqVcIUiDJzbsn3TFmQUlI6VBCXMB3oQ+vpqFW2Us18J0O7d14wInisprOv/kipextPKCn0rAEY8Fo2LlmDIWAoEI07PEDE7MQbpmLpz1FbYiGlBAX8MmnyEa8+JcWljjxzIaDeGgY8Mf/7ofHR4k6qXd381N31xQTxEE07N0LErOgDEQgJcQFfPIpcmHUgANGeNxU6sKb2ypgM4f+aqHZBYj5KUA8+sgSlCdWoYruNIfiHRLEpgxEICXEBT4/jfQkK+6cNBAf7j0RUl0nI9kStQwrg5ECDtiEx3CEnNRJyqzuS3fXisvJ1DwHUm0WFB+thon2A57DnYUqCPEDCWJTBiKQEgwPm3CWZbfimjF9MDXPAb+fxk1vfhfzPkYJOOASHtngc1Lv7uan7iyQEa24fIT3pc1M42/jga9+OoUrRvXTuHUEOSFBbMpABFKCoeESzmpb2rF6WwUoAJOH94YjzYaqRo/hU/pEEx6j4Wpo43yvO5ufurNApqZWPN6F/miHxIXv7QVlMht+PMX7MxQCSROnDEQgJRgWPsLZm9sq8Oa2CmQkWzq1fUYOOIglPHKxbMMhJFnZN8V4Mj8J2TS7s5sCn8pCj354AG1ePxxp0oSPeBb6fX4aO8pqsOi/B6KuQ0a3MER7ht01CPDGc8/A8k0/s75Hwzh7ip4gAqkG8Nk0pZ5Gu8NpVohw1nDahzQ9zJ9UL2Uv+SJWKKxraecUsuLF/CRE8Onubgp85k5tixcL39sLQLwAGc9C/8b9Tvzl4xLUtrRHvc7oFoZoz3De2t1IsSWg2dPR+Xq8HDa44OO/n5FsUbFF8QMRSFWGbTA70hIxZ/wZGJidjF6piahraceyDeI1CvGskQimqNTF+1pGyEiymPHyXWNxqsmN2pZ2ZKXYkJ5khc9Py34oUIKK6hZRn+MSsnx+Gn4/jYwkS0SOVgYjmJ+ECj7d2U0BAN74pkzQ9U4RAmQ8C/3PbCzFa1vLBX0m2mFSj2sN065YmvRgYRQIjJV5a3fjzkkDMTXPoZvfIgd8/fcbWr2GP3BpARFIVYRz02x0c6r+O6/huSHEm0aCa6EuLHFi9bYKQfdihIx3vqvAjvLakEj8cIFdj0K9z09j3c5joj8fLmTxOekbwaVBjODD9zBjBDcFoTyzsRSbf/xV8OdoCBMg41Xo37j/pGBhFOC2MOhxrWEQ6yIEAKu3VWD1toqI36JX4TsWQvz3jX7g0goikKqE2GAUBuZzi9YfgN2aABNFobrFEzKh400jwbVQL5k5HI9+VCL6vhtLqiJeCxbYAehSqN9ZXgtXo0fyfVZsOYxXvz6MrYdrYl5rBJcGoYKPz0/jo70ned1b724KfAgWADKSLKKEKQYhAqQRfZNjCUs+P42/fCxs7Qmv0hT8HRXVrXhx08+6W2sY5Hg24WurXoXvWOyqrBMknDPrzo6jNTBRFE41uZFttwEUUN3sMZQwrhZEIFUJKSfNYOpbvbhl9c6Q15gJnZ5kjRuNRDRN7/yCPbJ/HyOwL/3kIABKl0K9XBv3trLYgmhGkgUv3zQWEwbpv4Qo33555esjKD3ZgDN7p8b0+wOALLtFkJuCHjU/fLTgQnE1ulFcVsP6O4P7oLqJ3+FJL0I/H03lzjDLCh9oBAJguL6D6zN6UCDI8WyY37J4/QHUseSD1ovwHYvqZnHKgN/963u0ef2s7xlFGFcLIpCqhJJaAGZC3zFpoOZtkQM+fktKQAMxNZBaCvVqbtz1bV6YKEpzgYoPfPvlm8PV+OZwNWe98XDcXj+KSl2G9dsWkq9WCEs+OoBmj6/zb+Z3AoEDXfAcMlGAn6MBevJN5uvqJHbtXL7pZ6zZXs6rQAeDHhQIdS3SLTJA4LewCaPMe8HCNwDdHewAIDvFJupzXMIoYBxhXC2IQKoSSgoTzIR+/4dfJLVlZ3ktqls7NF8E5NImK4naQr3PT8NPRw8+kptf6lpRXAbdbQzhxMoJGA5fAa213RexWbBpQYtKXbpz8ZDqIhSNYGEU6ApiYSOaMArowzeZr6tTqs2Cw1VNor9HiDAajFYKBJ+fxrINh1T5Lkb4XrHlMN79/nhY0K/tdNCvXdN1aNyATN5V//jCjDmtNeF6gQikKiF00xQKjchoRzZyWDQSmw4FfCrv/Nf38Piozuu00u7oXYMLqKutVMLsyofHPzmI1vYu4SMjyYI7JuViweQhulo4zSYKV4/OkeQbGQ1msygqdbFmyHB3+HTn4qG3Qx1FAXRQJ+nJN5mvDzKfam9KoJVLgxZjaPmmwxGvuRo9Ia9rtTd9X1GL9g5ubacUtNaE6wUikKqE2UTh8avycN/a3RHJ2dXkylE5IRtjYYkTC9/bi+fGh16npXZHLz5lXLAJ9UqhlNmVD8HCKBAw4zOmx2dnj9SFMAEE+uh1hYRRRhj54wf7sH73CdYMGXw+r/Zmo7dDHU0Df54xHL3SbOiVmohxAzKxq7IOH+89obn2XW99xaC1S8OXAtLqqYnce1O41YMZm8zftU2tAIDfvf1Dp8JGCd7aVgY/TXfrgCcikKrI9PwcvHrzWE20XQyrvinH2f0zMWNUDto7/Hj0wxLdaXcYbbKeNDzBTB/RGzvLaxVfMJQ0u0qhvtWLeWt3Y+GUoVgweaimi6ZaffTf3SckfV5toUePh7qGtnbcfdEgFJY4cfHzX+nG31aPfaW1S0NhiRNrBKbVUws59yY261O437PNTONv41k+LDNflP6KL0q70rGF5yfvDgIqEUhVZnp+TkiptYrqFrz57VE0un2xPywTC9btxl3HB+K/u0+gtsULm5n9OjW1O+Gn1CtH5WDVN8povaSyZnsl1myvVHwT1ZvZNZzlmw5j3c7jmi6aeu8jBjWEnuA5lG23wZGWiKpGZVyExLCzvBbLPj2IN1kEHaUtMiF9k2KD30/ju/JaADTOG9gDjrTEmNpuNVHapSFaRghGUaFn5NibuKxPXH7PahOen9yRloilV+vDzUUpiECqAWYT1TmJ2jv8eGt7BQD1BFI/Daz6poL39Uprd7hOqXpH6U1Ur6bEYMIXTbb61tkpNoBGRN5cOdB7H6lldi0scUZEuGckJXRqk/Swx+6sqMPOijrW95S0yMTywV6BMiRouOAwz2fhlKGqBO5EywgBAI9+eEBwaiutEDv/9Wp9ioarMRA8+Mrcsci0W+FqaOusNuhIiw8NKhFINaSwxIlHPyzhTIehF5TU7uj9lBoNuTZRLm2FHk2JsWDqW0eLRpVTs6z3PqKhvNm1sMTJGuVe3xYIckxLSkBDW+yAR61RwiLD1we7Q8MFR80Ar1h16Y2G2PlvFMsKG/ML2J+T1mnm5IAIpBqhZbAKX5TW7hjxlBqO1E00mrZiap5D9jQjSsM8y2htllOzrHT2CqlkJFsUvb/PT2PRf/dHvabRAMJoMHJpvfW+vtx/yWBcMLSnapotrfI7K4HUvUnvlhUxOOMgp6lJ6wZ0R/S+UAajlHbH56fx1rZyw55SwxGzwDGHkvA+YAS2vxUeMpQwypfg3Hs+iZopJnuFXudSQ6sX963djcISpyL3/7/Nhzs1oVzotW+4qG7ySB4XgAG0YBRUNbPqvj8EImVv0rtlRQpyrKtaQQRSDTDCwtDDblXspFVY4sQFz21RLemyGlQ3efDx3hMoLqvhtRjE0lbQgG6DuuQgWLMslT3H2P0S9QDzLBf99wC2HamWdaMoLHHipc2ReRuNzrINh3DBc1skC/F614K9/FUZxj1ZpNhhJRy994cQHpxypqS9ibGsGNvjMhI511UtIAKpBmzSaX43hiy7BcWLL1NMGGXTChoZExXYRB94dy/mrNrBazPlcygx6CFXEFI3yY37nYolxJeT+jYvbnrjO1kELaDrQBOvMFYCKX1lBC0Yk0JNDaHUCP3Bl4HZyZI+z1hWAMSdUAoY9/BBBFKV8flpfLhXWk5DJaEAPH3tSFgT5B8aRnJVEEK44MhnMzXqgiE3UjZJn5/GXz7Wd3qacJwyCFqAMawsUpDDraOupd0Q2ToAdcys8aQVlEO4ZvKCpyvs560FQi12eoEIpCqzs7xWtyk1spKVM9MD8beJcm12fDbTeNJWiIGC9IpXgbnULl+jVIIGsHj9AUkbRXc40EgxPxaWOHF/wW7DWBnUMLNG0woaSUhNsSXIFmg7Nc+BRAWUL1oixmKnF+LrSegcn5/G28X6NS9eOSpH0ei8eNpErxnTJ+pmF2sz5aOtMNImwcBElfP5XVID5ow8nupavVixRbz/Z3c60Ah9zka1xCjlyuXz0yguq8HHe08gPcmKl+eeDUd66PhJT7Jg4ZShGHtGuiJtkJNmTweKZOqrneW1Ibl74wExFju9QARSlSgscWLck0X4vKRK66Zw8u8dlWjv8Ct2/+wUm2L3Vpt+mfx8mLg201jaCgrAlaMc4huoAQsuHYJdf5mKlTePjdjwgnGkJ8qiiTe6ULZmW4VoLSlzoOkOCH3ORrXEfLj3hOzmVSaAdM6qHZ0as2UbDmHJzDw8eNlQpJwu01ff5sXyTYex+1iDrN+vFHK5OBj5UBsOJcFipxdIHlIVMELOUSAwcB95fw9G9c9UpvqD3juAB0z+u4mDe2DFV0diXh9tM2V8mMLzkDKJstu8fny6X98BcMFMGpINs4mKKI+rVKWmMf0zkGgxwe1V7hClJPVtXtH5a5kDjRGTmQeTkWRBQ5uXdWkQm2vSqEJGbYsXb20rx+2TcmWZH9GS4HMlVzcKchVQMPqhluHCIT3wzZEazvfVLAMuBSKQKozRzEcf73fh4yAhSM7qD9UtxjeNMJV3JgzqETUhO9/NNFx4YwS2olIXln12UImfoAhZdgvGDcjs/Du4PK4SPLWhNC7SYkkRnqbn52DlzWOxaP0Bw+arvf38AXhp85GI8qZS3DqMLGQs23AIb3xbLnnNjack+FzIcfBgLA1G1KgHM6p/RlSBlEHvhzVislcYo5qPGOSKCgaAiuoWGVqkLclWc2eZ0FgBAnw3U0Z4mzWmLyYO7oGiUhfuW7tbt8FvbNS2eHHR377CS5sOKx7deffb38eFMAoA2XZpbizT83Pw9DUjZWqN+ow7Iwuvsrh4SHHrGJ+bhYwk40ZOy+HzZ/R9hw9yHDzMJgpLZg6XoTXaQvGMOND7YY1oSBVGqRPJ85iL2ZaA3whNA+u9wCMoUOS7AHnqta/beUzmVkWidL+0tvuwYssRPDBlaEyTe/BmylWvPhwlNepK942r0Y3lm37u/FuJ2sqf7T2BotJTst0PUH8uhSDRMuvz01i2QZl8pGr0y4J1u/HcdaPw7Z8m85offDCbKNwxKTdkLMqNkn1DIzAspKy5WmnC1JpLFIUQi4wUMiUeCvmgdL+cl5uF/+6WbrHTGiKQKowSJ5Iyy1yYTKFOzNeZgNn+uRjslX/yy+F/okY0o1r9smZ7ORZMHsLqL8m2mUarVx8urCml2VB7zADy1qwHAsLX4o8OyNCyLrTol2Cqm6XNCaOPlwZ3hyL1t4f2SolwA5ALNfpG6pqrhSZMzblE08DLXx3GwqlnSb6X0oVq1OiXRz7Yh1lj+uD1reWyur+oDTHZK4zc0bDM4GbDZAq8rxRSTt1Kn9jV7Jf6Vm9IOqdwk3u4MBqtXn24Wc7V0CZbOxm0GjNyR3fuLK9Fk9sn+T4MWs4lBqmCgxLzSot+kTMCmMlBqqQwyoYSfSP2+aqdBF+LMfN/m49g435prmSFJU68ua1CngaxoFa/VDV68PrWctxzUa6s7i9qQwRShWF8DeVYGJ5H1+AOT/HA/G0yBa5TAimbp5Indi365Usep2o+gQXBG3FhiRPLNhySrY2A9mNGztrKcgpfWveLHIUBAPnnlRb9IucYUdrlRe2+Eft81SyNqdVcogHMLxDva6t0+V01+4U+/d8n+5zY8odLsGTmcNw6cQCWzByO/z1yqSGEUYAIpKowPT8H91yUy5knjC+MDwrXfZj3Zivgz2+S6LOj5Ildi355a1sFNu4/GfWaWObU4I2Y0aTKXXlIyzETjBzCZEV1qwwtCaBlv8hpQpN7XmnZL3KMESWDedTum2SrWdKBhfFxj5YTWA60XmPEateVDvzSol+cDW5Mem4Llm04hLeLK7FswyFc/PxXhkiKDxCBVBUKS5x4bWs5aInHdr4CrVTBlw0/DeyqrBP9eSVP7Fr0S+B0vkeWevWuhjbFtDpajplgpGryCkuceFHGIBUt+0VOE1rwvJIDLftFDm2vkq5BavdNa7sP249US3JlmJ6fg2//NBnr7p6Al24cg4VThsrTuCC0XmPEateVdiPTql/ClRrB7mHBVbv0WOeeBDUpjM9PY9F6eQIx+Aq0UgVfLqROYK6o9Cy7VZJmUMt+iRYJy3eD3X2sTrGTutZjRo7oTiXMsGr1S0ayBU9fk49Mu02WCHI2pufn4OW5Y2Xxm9RivMgZAayka5AWfXPL6p2Ss1WE5wRube/Aa1vlS5um9RoDBAKThAZ/KR34pYd+AbqyNixefwBLPzkYElysRCYUKRANqcLsOFojW9Lq9d7A4OUawMx76xVKXylHHtHwE/u6uyfgxnP7S7qnlv0itV69iQL+vUO5dFhajxlAumlaCdOaGv1y8ZnZePzKPGTabRifm8Ua9CYXh081yyKwazFemGITcvSLkq5BWs0lOWuR+/w0Ptknr/lWD2vMm9sqBPeP0oFfeuiXzu8CUNfqjch0o7c690QgVZjistjVE/jyCArgP10lMXyQM3/7/crlUFy385gsKv7wqHSp+5DW/SKmXj2D0hYTrfvmrkkDJZ++lTCtqdEv//u5Ggvf34c5q3bggue2KLboF5Y4Zcu5qcV4efCyoUhPsko2IzK5fmfkOxRxf9FqLsmZrUKJw53WawyD0P5ROvBLL/0SDbkzoUiFCKSKI+9DHuztGuTh+P1QNHeiq9EjSyRsOBMHZUu+h5b9Eq3aDldggZrp4LTsmze2VeCZjdIiWZUyranZL3JruRg/sG2Hq7H0E3kjhdUeL69/cxRzVu3AA+/uFS28F5Y4ccFzWzBn1Y7OND5KzDGt5pJcmQiU8pvUco1hENM/Sgd+6aFfYiFnlgupEIFUYeQQtsIZ7C3ABx7A5wsMap8P+MCjzuCWuqCxOVU3tMljs9CqX/7wn31RN9BwN4UlM4crrhkNR8sx89rWckn5AsfnZiEjWZkQXbX6RS5NRLDg9cC7e3HTm9/B1Si/kKHmeGltD80tK1R458r1q5RvnpHXXyX9JrXsFwYx/RO8Pt86cYDsbdJDv/BBD3XuSVCTwkwY3AMpNjOaPfIl9AYCav5HNCh1LmVBY6tY5Eizwd3BcYQUgRb9UtUYuyJRcGDBx3tPqNm8TrQaMwCw5OMSTMsXX3pWSdTqF6nVdxjBS62zjFbjRUjpTD65fpXAiOsvEDjcSQ0ijYaWawwgLW8rMyffLq6Us0kAtO8XPuihzj3RkCqM2URhzvgztG6GZMQk8g7Whr606WfMY6tY1OiRLehLK4Rqv/Qw8dWmpqVdtEloZ3mt4cdIMGI0EUomfNcjfM2ISueS1AtyFVIwmyg8OStfnkbpCLn6Z3xuFjKT40tP50izISPZwuknK1ffyUF89bwOKSxxYtU38qXY0BIhkbBs2tB4Roj2i4nudDW4u42AAYg3CenBlCQnYg4k3UXwCifWs4+3scGG3LXIZ4zKwb2/5Mqa+klLpPQPEwgXnJJt2dUjseDdPfI3VCP+fv0YNLm9uG/tbt3XuScCqYL4/LTsAQdakJlswTOzR/KOllbbtKgn+GyQTHQn2wIRz4jVDMeTRlmsJqI7CF5sxHr28TQ2uHAokCty8Yw8+Gkaq76pkO2eWiG2f9iUJjnpibjxXONbNIOpbvZg1pi+rDnAlRhbUiACqYLsLK9VJOBAbfwCgjC6m2kxHL4bJHeRAAsm5GZhY0mVUk2Und9NGog3Tkc2cyHFJMRolONBQyhWE9EdBK9g+CbLj2VtYO5jxLGz4NIhmDQkW/ZCCkBgnf5sv0vWe6rNgksHY9KQnqL6h0tp4mpwy5ZCTS8wa8f0/BxMzXNEaIT1oBllIAKpgsSLVqPB3REzaIehu5oWxVSb4VogAGDPc1t0349ZdiuevjYf0/NzYDZTnCZACtJMQmYThSUz8zC/YLeE1mqLiQJWzDlbtCaCj+DVO82Gv18/BlsOVWH9nhOoM6jfrRAzYjRrQ/B99hyrM5yJemjvFFHBb3ww+jptooC8nDRR/aNVIJzasO1J4VW79AYJalKQeNNq8AnaiRchXAhS/HDCiwSYTZTs9cmVYsnM4Z0C1uIZeXhl7lhk2a0h1+TIVLc9M+y+RuOlG89Gpt0mOvl7tCTezN9Lrx6BSUOycW5uFmwJZumNVgm7LbStDoFjhiuXZPB9Fs/Iw70X5SpWT10JlNw/jL5O+2ng/oI9ovL6Gl0Y54PefEP5QjSkCjI+NwuOtMS4MNvzDdqJNyE8mESLCS/8ZhSe+vxHxf1wpufn4JW5Y7Fg3W7Vc5bypVdqIorLajq1u9PyHZiWr4xJyKgbaA+7FdeN64unNx6K8FUTOma43DyCx19hiRPz1hpLk/zkNSPhSEuUNGb4mCMXz8jDHy4fhkfXH8DGA060euVNxScXYqwtQomXdZpParBwjLqWCCHLbsVTp61XRoIIpApiNlFYenWe4TaIaMSazPEcQf6P347BjFE5uGJUH1X8cGaMysEKnI35BfqL+MxItuAP/9kXctgSI2TxxYgbaJbdgieuGoHfv7uH1VeNrxtMMNEEL5+fxqL1B+T9ESrgSEuUxYzIxxxpTTDhhetHY9k1+ch7rFB3a5Ramq14WKfF5vXlu5YsnDIU735/HLXNbSJbqB1/CbJeGQlisleY6fk5WHnzWMUqzahNrMkcy7RIAbhubF9F2qYkiRYTpuU7ALCb2ZVixqg+WHlzpClca+pbvRGafznLY4bDbKBGMT5RAJ6clY+nPj8U1VdNTOUmrvG342iNofK1apn/sOC7Sl0KYhnJFllcXGKhdB13NRGq8Yy1ljDjcsHkofj2T5Ox+rZzJbdRbRzpSVo3QRREIFWB6fk52PWXqXjnd+dhwaVDcP8lg2FLMNYyIGTziOXT9dx1o5GRZCwB3e31a1brd3p+DpbMHK7Jd7MR7vPHIFd5TDaM4lcLdAkVmXZbVF81uWtIF5fVyHIfNdDax62ytlX17+SDLcGEqXkOVb5L6TruUrkivzev64RaT/j4YzPj0myiOve8mwxS4MaRZtNFknsxEIFUJcwmCpOGZOPhaWfhocvPQpLVON4SYjaP8Prt6+6egG//NDkQkW2icMekXOUarBBa+h7p5cSbZbeiJUoZXLmFrGCYDTRT59aGJIsZU/McvMeLfONKjzq/wPoRbiESGrgkNwOykjX53li4Gj2qHnyZdVpPB96c9ESsvHksVswdx0uTKUb44hMIF85lw/kJyFqz9OoRhgpkCsY4UlEcYbRSiGKDdqL5dC2YPARrtpcbqh+09GPUg88XBeCaMX2wOkbOUUBZ4d1q1vdiywjkfMeLXONq4qBsrPiqTJZ7ycnLc8cqFuwmllsmDsRTGw/pMmBQ7YOv2UQhO9Wm6ndyccuEM7D06vzOscEnpZfYcSQ0L+e4AZnIsltQ26LPPSs9KQHP/WaUIX1HGYiGVAOMFOW34NIhnZpNOTGbKDw7e6Ss91QKPdT6ZcxMWu2fmafN0HzNiUoI70wy66qmdtnvLTenmty8fdXkGlcTBvfQla86o+maMSpHVb9rPlgTTLj7Qn1aadQ8+Pr8NIrLanC4qkm17+TCRAFLrgzV7onRZApByLg0myg8OStf0vcpxUVDs7F7yeWGFkYBoiHVBCNFDE8akq3Y5sEEfEWksUmzwd3h15X2VA/53KbmOZCRbFG1X5KtZtx70SAsmDy0M5KbT2UcuYV3vhXArGYK7T7t1V69UhN5J22Xa1wxhzyts3pckd8bt07M1VwLGotA+Uxg1Tf6SZifZbeodvBlK52pJXdfmAtrQqSOTE8VhkwmCslWM1rb+aUMM1FQXAtPUcAbt52r67nGFyKQagCfqiupiQlodHdI/i5mIxQqyKiRCw/gXmyKSl2spd3URslURkJR29XjnAEZeO/e80MWOrWFLAa+yaxTbBbUtmqnQQ2fN3xyh0rF56dD5s8rc8fir58dhKvR03lNZlIC6tqkryexcKTZsGLuOMNsjn+emYfRfTOw4F15UqvZbWa0enyi163fjO2nSt9xlc7UAhMVEEYXz+AOWtRDhaFNh6owv2AfZ5/ZzBSuHN0H14zpi9rWdvRKTcS4AZnYVVmHTaUuvMnD1UkM93AI8kaECKQawGdTnz22L97aXin43hlJFtS3dQktzMYXLPRVVLdi3c5jnAn71Y6AZVtsuDby1EQzmtzKJrTuYbdi1pg+mJrn0JWWR21XjxP17N+nhpAVDt/frrUwCkTOGyU1PGxarpz0RDx2ZR4y7bbO7xs3IBMTntmkuP/brDF9dDNf+HLlmD5ISKCwaP0B0Qc+u9WMe05bEp79vBSrvqkQdZ/P9jux6IrhivYhX2tDOEr4T14zpg/+dt1oQwhUz37+Y9Q+y7Rb8bfrRkc8u4mDe2Di4B6gAV7+93zhI8gbDSKQakSsTT09ySpKIH35prEwURTrxhcs9C2YPAQ7y2uxqdSFDft/AdAl5CkpWAiBbSN3Nbqx8L29sn5PVrIFt50/EAOz7boIuOBCbVePaEmn1TajGcHNJdq8UULDw6XlcjW4cX/BHrx681jMGtOV8/faMX0V09IwfLLPiT9OV1agUgJmPO8oq8E731VgY0mVoM+nJiZgweShAIDP9rtEt0NMoneh8LU23H/pYGQlW5Flt8KRnoRxAzJx8fNfyRpYecO5ZxhCGAVwWoHDPa6ZDAlcz25qnkNWgfRft4/HhWf1lO1+eoAIpBoSbVPfuN8pyP+EMRVOGMQvYIDZICcO7oFHLh+KLwo/x99+Mwq90u26EsjCN3K5cy1m2S3YsXiKIRZFLSLto2km1TSj8XFzydQgAnbJzOHITrWpfpCJpuWiEeiP8LKKaUnKF1dQQ6BSCrOJwqSh2Zg0NFuwf2VwuiapPplKW0L43v/M3qkhBxqgK+pdDrQOFFWCaH3LrGFy+ezWtuk/uFMo+t+F4xy2KL/CEifuL+Bfw1yqiZ35zIyRObqIgI2GnBV7KABPXzvSEMIowC+hczhSH6VeNJN8fvuTs/JVq+bERMjfPilXk8jxWFqu8HywhSVOvLjpZ1XaZqQsIlwE51G+deIAXp851eSW5bcrPeekpCNjLHs5MiTT10OgqNxE61u5i3voZW2WE2PsxN0IMf49WieZVhO5St7lGLTPoqVBefGGMQCA1bed21mM4K4LxKW20UOqq3BipYCZMaqPKuUQta4yBPAX+k41uUX7DIolXjZKRllwBc81oldqoqTfrtack5qOLFhYX37DGCyZORx/v24UsuxWXvPORAGvzDXe2utIk57CbXp+Dl6Ze3ZURQGFQOR8tPf1tjbLBTHZ6wy+/j1amQr1AJf/bU56Iq4clYP/7j6B2pYuc0Zqohm/Obsf+mcld/pDGbnPuFw9/L4ObCwPbDgWiwU+P42H3t8r+P56ELi4iOW7yjU2+JBlt6CuxRtTcNODj7UQLRffNSWczGQLbAmmkGj9aKiVmUNt+LiLBP9u5tpoqJmhIhw5MmWwuevYExN4mfNXzDkbM0YZSxgFgEVXDMP8gn2Sn92MUX2wAhTmF0T2FfPpey7MxetbA+nItBonWkAEUp3BV/ORnWqL8O/pTkQTTBZdMVwXOeuUhG1D8IclH+AriIRHz+pB4IpGLN/V8LGRnWLDH97fi6pGT1SBYsnM4bi/YE/EhsNw16SBmKKTzAtChKTP9p8UfH8KwDOzR3b2o6uhDbUt7ThW24p/FUcGW8bzRilUgAu+FizX3nNRLj7Z51QtQwUbSmTKiHUY1FMKPTFMGd5btj6bMSoHK03R73X2GZmqZjLRA0Qg1Rlqlxs0MlyCiR5y1ukBvoebJVeOgCMtMa4E+PAxsPTqETEFiun5OXjVRLFq3vW2CQgRkoSuFeG/N3wuTRzco9ttlEIEOObaZzYcBNDCeu0fp2t/aFYiU0bwPV2NbtQ2e+LCKsUgZ5/xsfbopSCAWhCBVGcINQ8RCFzwFUQcaYlxL8DzFSiMtAnw/U181pQsuxV/mTmcl+BgpD6SEyG/e3p+Di4Z2oMze4leDs1KtEMvv00p5Px9se4V730ZjioC6csvv4znn38eLpcLo0ePxj//+U+MHz+e8/r//Oc/WLJkCSoqKjB06FA899xzmDFjhhpN1RytKuEQ4g9yuAmFr0BhpE2Az2/is6Y8dW2+IO2mkfpIToT87uDsJRaLRclmEQhxgeJR9u+99x4eeughPP7449i9ezdGjx6NadOm4dSpU6zXb9++HXPmzMFdd92FPXv24JprrsE111yDkpISpZuqG2JFE8erWYwgL3xSJXW3ww1bmjWjw+c3kTWFQCDoHcU1pP/4xz9w991344477gAArFy5Ehs2bMDq1auxaNGiiOtfeuklTJ8+HY888ggAYNmyZSgqKsKKFSuwcuVKpZurG7qrWYwgL1qU+SToE7KmEAgEPaOoQNre3o5du3Zh8eLFna+ZTCZMmTIFxcXFrJ8pLi7GQw89FPLatGnT8NFHH7Fe7/F44PF0pSVpbGwEAHi9Xni96lZtUYJzzkgDkAYA8Ps6IiKp5YDpp3jor+4M13O87KxsXDL0QuyqrEN1swfZKTaMG5AJs4kiz1yHKD0f1VhTCGRdjRfIc5SGkH5TVCCtrq6Gz+dD7969Q17v3bs3fvzxR9bPuFwu1utdLvb6wM888wyeeOKJiNe//PJLJCcni2x596SoqEjrJhBkINZzrAbwxSF12kIQD5mP8QF5jvEBeY7iaG1t5X2t4aPsFy9eHKJRbWxsRP/+/XH55ZcjLS1Nw5YZB6/Xi6KiIkydOpU43xsY8hyl4/PTrJpkNSHPUV9sOlSFZz//Ea7GIJeXtEQsumIYpgzvzfk58hzjA/IcpcFYrfmgqECanZ0Ns9mMqqqqkNerqqrgcDhYP+NwOARdb7PZYLPZIl63WCxk8AiE9Fl8QJ6jcHx+Giu2HMGabeWob+syMWmZg5Q8R+0pLHFifsG+05kJug4mx+o8mF+wj1dAGHmO8QF5juIQ0meKRtlbrVaMGzcOmzdv7nzN7/dj8+bNmDhxIutnJk6cGHI9EFCVc11PIAjF56dRXFaDj/eeQHFZDXx+tap8E/RIYYkT454swvJNP4cIowDgbHBj3trdeGnTYTJOuhk+P40nPi1lTZnGvPbEp6VkXBAIMqG4yf6hhx7CbbfdhnPOOQfjx4/Hiy++iJaWls6o+1tvvRV9+/bFM888AwB44IEHcPHFF+Pvf/87Zs6ciXfffRc//PADXn/9daWbSugGFJY4DVGJh6AOhSVOzONRf3v5pp+xbmclll49goyTbkKs0rs0AgeWneW13TInK4EgN4rnIb3hhhvwwgsv4LHHHsOYMWOwd+9eFBYWdgYuHTt2DE6ns/P6888/HwUFBXj99dcxevRofPDBB/joo4+Qn5+vdFMJcU5hiRP3rd0dscm4Gty4b+1uFJY4OT5JiEcYDRhfXI0eMk66EXxL7/K9jkAgREeVoKYFCxZgwYIFrO99/fXXEa/99re/xW9/+1uFWxV/+Px03NYQlorPT2PpJwc5zW8UAua3qXmObt9X3YVYGjAuyDjpgllz4jGvKd/Su3yvI4hD72NM7+0zEoaPsicEYDNFMwgxScfr5Fqx5QhcjR7O97uT+S1en7FQxGi2tBgnen1ebGtOlt2CJ2flY8aoPhq2TB5ild4FgIwkC/w0DZ+f1sUziTfUdLESM8+IC5i8EIHUQHBNGMYUzbVoOk+bpGNFhG46VIW/bvgp7iZXYYkTyzf9zOtaI5rfOsdFQ0vn31xxjUIXUL0KQ3IgRbOl1jjR64bHtebUtngxv2AP7j5ejz/PzNOkbXLBlN69b+1uUADr+lrf5sVNb3yni2eiFkLXBLFrCNcYc/Hcz4QQbZ5ddla25PbF8zoqJ0Qg1RnRhE62CbNk5nAs23CIUxhloBHb1Ljwvb1w+0LfU2LyC0HqRBbqJ2g081vwuLCZafxtPDDtxa1YPDMy+IYrgIfrGetVGJILPhowLtQYJ5sOVQWlHOpC6pyUa05F67NV35QDgGGEUq4+4Sq9G47W66RaCF0TxK4hsTIcyOliFUuwfGXu6IjPtHf48eiHJbzaV1Tqiut1VE6IQKojuExgo/ul46ufqiOudzW4Mb9gD+/7c5kambQlevOv5LOYdfrNNrShtqUdWSk2ONK6NhQhfoI56YHPaQlfYSGQN/Mwlm86HPFeVSP7CX3R+gOs38k89z9/WILJw3rDmmBSVTuhFcEaML5QABwqjZNnP/9R9jnJNqcykiy4Y1Iu7rtkMHZV1sUce3zn1KpvynF2/wzdm+9jrTPT83MwNc+BHWU1uL9gd0RqMKB7+KELXROkrCFqZTjgI/g++/mPeGhY1+uFJU48+uEB1LZwl8Rk2rdiyxG8uOnniPsz6eRemXu27ueHmhCBVCdEM4GxCaMAuwAZi6JSV8QE3lVZF/Uzck5+vpoZPouZ30/jLx+XsC4MjrRELL06D54OP+/2LZk5XFOzCl9tQmGJE0s/KQ2pHBNMcI5EZnNcseUI6luj1xSuaWnHhGc24clZI7FsQ/T8i4vWH0CqzYIJg3sYYvMND/jLSLaivrUdWXYrfjO2Lz7YfYL3vR6/Kk+V3xx4vuzfI2ZOcs2p+jYvlm/6OWLj5NLiCHFX+MvHJZj2/9s78/ioqrv/f2bCZLJANqLMoEjCJkvYKxKjWCEIhgoubRXQWrWoVZ6foo8KrVQsWlH7PGiL+0atglrFYgXjw9YiyKJAhBBUCAluCZodsk5m7u+P4YbJ5N475957zt1y3q+Xr5ZkcufMd87yPd81x2/aHIm158jJJDrMKc7tgtvtklRGRZwch06aFDp5aB/sOVaLyoYWLP1A/vUAsOT9g7LKu1EVDkgU38h9NlZ4XDSvbi9TfO381fuwAi4UjLL35Z4WXCG1AErmf9q8s+dbLLxsGOJ7nK74VXVSPtknEj2LX43rhuTWes8/Pkdja1D2/SobwjfQBflDiMZ3+Sgflq47ZJpbhdSaQLohRh6OE7Iz8MLWUqJxhGMAY1sM65oCmPuyPWLnlBL+1JDocWNCdm9sP1KFxpZ29E1PMj0WjHRNkrjZSS1ZasIVahoDpilosfacWDKJDnOqrG8mel87xqHHgjQpdOKjm1DT2Eb0zMqGVvxl02FMHNC7y4XBqAoHar4rkjUUjdIFBgBCAnD7qr14zm1/jxMNuEJqMiTmf5o0tLRj4qOb8KcrczoWQGZPL6RtsJ3RuvjVum5Ibq1KymgkL3xcCl+KF8cbWmU3kuT4OPxrf2WXnxvlniaNl5o8tI/qDfGHEy3YebQajW1k8lKL1V34ai0aSjQHQvjPVz8CAP6OrwGYHwtGuia1lLjqsIa/ewDJ8T3gdrtQdbIVmclepCd5UBvD4i5ihoJGsuekJsbHlIl4qatvbsPitQeJ3ru8qqnLz0i8Q1ZNfFGTFEqqjIo8tekwntp0OuxIXE9Th/sU47tphc6oOdM+1VgmjgQnh3qogSukJrJ+//eqYkBpUdPY1kmJGN8/HR8dknMO6lv8WoLTaR5gja1B/HTIGVh/oFI2U1ZOWTMqLow0Xupvn5Sr3hDP7JWAjw//qHOE8lg5dk6LRUMtYizYcwwUcl9KAr6ulb5IqV2TetZUXXMA17+yu9PPUhPJjw6jEwVJ95z/nkrmPfm/gxV49ZNjxO//5MavcK6vZ0d2Nkn1EqsmEKpNCtVL5IVBrsKBuMPQCJ2JldjoQngdAo245x+f63ovJSrqW7Byexl+nZdtqT3UaJh3auouqO2Pvn5/BeavNl4ZjUTswxy5AKKXgt7FT6psLXm/GH9YW4yXPz6KjKR41e+jxKZDP+Cv146BL1X9wRjp+mYFqbLwxEdfqHpuWqIH7+z5Bi99fFTLsIhhKSO16yryb5Zv+JKZRSOahWsOIBgSNI1X9pmXhTMpaKxJ2kphfXM70evSkzyGJwqS7jlF39YRPe/NT79R9f4CwvGR4ne/4K0iye5wt72+F3/810E8tfGwZTrIRc/fnaXVhq0hoGv8+7PXjeuyb/tSE6h5ZMTERkB+nRXkhLtK1rew9WIuXXcIFz62uVt3guMWUgoo3W6nDvd1ccNsKKkkitNjSaQS8ZNzUgAAy68Z0+Um79N5SydVtv6+8+uO/+9C2I1Oy83c0h7C3f/4HLdNGojxWem45e+fobVdnaJA8jm0utxIlYW2oLox1zUH8K6KZB290HbNarEa0YoXVUtdUwDXvbgLZdUnO8Xa6bFy5Q/rI1lySMuaFC1BRsslECRPKqQF+TwkU+abA+o/Q2VDK57/TymyIF+9BABe2V4u+wyjvQ9SaycpPo7pe0ohnk07j1Z3VDhgGcogV9rLl5qAxTOG47EPD2LoUIUHUIS0ZrhT4QqpTpRilW57fS/Skjydspt9KV60qMj8Zk148w4rpPnD+uDSnLOoLn4tlhkB8m50rbQFBfxlyxHNfx/rc+hxuemph2klaFrhtJSMoRkvqoUdZdVdfqY3xlbNgSx3IRJ/flmOT1EBYsHJ1iB2llYjb7B0cXEWkM7DE4wtXs/8pxSPT9D3DKMy9+XWThOj2HMS7nhjL5ZdPRLTc/zMk+Lk1plYmcNorBgCZQRcIdVBrFglAF1K7ShlKprBZ+U12HesGmMRzvZP9rioLn4nKFux6pPqrdlJ0hHG6tCs4aol7tiIeFEt0LByxbljr0m5C9HM0X68/3mF4ZbRSO5YdVqxMALSPeefRd8bMh4asEwMs+raqWsO4Lev78Vd+UOQlZnEPNFLap2ZkZAXXSHFiolurOAKqQ60ZK5ajb/v/BreOAFjJwDjH9mAvEFn4jcXDaQ28Z2gbE0b0adjc5DKjKXRUUR0GynVF7UyM0fTqzWptih2MCRg5fYyy65FcbyskhaUamk+v7WM6ntpQVQsnp4zDunJ8cwPVyfsOdHo9T4ohRNZ+RwTgE4Z/r6UBMyecI4hCipgbue+/ztYgQVv7etkxPKleLFkZtcufE6BK6Q6cFq9OUEANn3xIzZ98SPVDE9R2frde8Wqy4JYgZWfHMPKT45JyoRmR5HpOX708now9+VdtIZuGC9sLcPYc9KpzBc1RbHDTQIOWs7zIMXSdYfw0rYyqpnTVrVuRSMAuGP1XggRA2WZRU7a8tMO6E0MixVOZKdzrLKhpZOCSnMOSSntE7Iz0KdXOMveaKQqO1Q2tDKr6mEFeJa9DuzW91wNYnA1rYy/6Tl+LJ4xjMqzzEIs8fPUxq86MmhpdxSparS+YiWHWLVBL6TrqryqEbe9vtcWyqgIjczpyExoK1uGoxGipgbrLPLpOX5su38yVs+biPmXDGTyHkZQ2xTAhpKudZJJEK3nShn8mcleGsM0BVpzqLC4Ahc+thmzX9yJO98swuwXd+LCxzbj8cJDaG03L45WDrGqh9PgCqkOxFgl50Z00FMyAMCXmkjlOWazfONh5C0Ll+eg3VHErpccmqWfYq0rF8KWkVe2m++OVktkWRst6yr64Fy67hDdARqIXlmQIMYFmpmcoxcx7EetjEhyHB76VwlC0TcFG0FjDskp7WLIS6xuS2ZQ1xTAztKuSZR2hyukOlCqYeYEaNeXdJICX9kQvpnXNrYSKU+kLrcJ2Rno6TW+1AotaLj/SGoDXvOTfsS1MK2G1nUld3DaGVEWr2wro1K7VYrC4grDqwvQROt8IQ0n2sWwxrIR6Dmn7BLyIsX2UnYNT8yCK6Q6EWOVoov3pid5TBoRfWjFGEUqGk5AQDgucPEMZeVJTQHzOLcLv7lwALUxGg0tC6/cuhKLYreHrFM6TSus+2jbiUfWH+rkKqXlxje60xBLfjjRoqrxAvn8csas0nJOWTmhKxavbCt3XBF9ntREAbkaZis2H8byjYdjP8Di0HQjT8/x45ZJ2ZbI/qVBRX0L0pPjqRUwB4D/mjIYL3x81HZuRtpdeZRqcBZ/10DtfcxCzbqy88GpFr21WyNxktzKqxpx4WObiWsdk86v3AGZeHfvd7YuzQdoO6fslNAVTUt7yHEJTlwhpYRUDbNzMpJMGg09kuLjqCoZwZCA9z931q2usr4ZV447W3dHkcgsz1snDeyUTWoHxOQLmpujXA3O3IG9sUJHowMzUduHHrD3wakWmh2KnCI3lwuSxg0l5Z2oT3tqAs7LzsC1551ju/1GRMt6ErFrzH4kTiqizxVShtixxFE0zW1BtLWHkEiphdzOo8b2RjYC8XsmKWAuh1RplrQkD9raQ7aylBq1OZ6XlYFkbxwaW+0jm0jUhHEAzjg41UCrQ5FT5CaXd6SkvCvVYxVfNXO0Hxc/scXWe7IA9etJxAmNW4zo5GUUPIaUIelJ8WYPQTcCgAl/2kglVqWwuAJ3vLFX/6AsRkZPfWVT5JJV6poCaGoLIje7N1IS7HF3pJkEJxIdN7d+fwUufmKLLZVRtwu4ZVK2aiuykxIC1aDXwjkhOwPeHs4+5pSSeuRisVMTPfjZKB+e32qfsmEscEpislM8AfY45WxIYXEFHl7vjGD6Ey3tHW4hrW5ps/uMs+TMXl7sKK3W5KonSVbZUVZtq81STL6g0fJOynJsZ0KCtiYCTuw+RIJeC+eGkkq0tts/AY4EKaUkGBKQmhiP+6adi+1HqrDh0A+obw6grjmAf+3XVtvUaugN73BCEwWneAK4QsoAI5SvJzAHV3nCsUWCAKwJAPdiFcN3BBatOdClKw5JpwwjM4SNlkt6kgf3vF2kWiYipEkXNGRnlGzKq5pUJV/IYdQlxoy1pOUAlTs43a6woksbM+QioicuUCQYErDk/YP0BhWBmbKRI1opMeMyZ4ZcaIR3RCZQbj9SRT0+naVcenp7UM3zMBNn+zJMwAjlq9QzBz/3AnFxgNsd/t+fe8M/Z4WAcNJKdFcckk4ZRmW6miEXrTIRMcrVYpRs0hJ74MmNXyl2hiHBqEuMWWtJa2hDZPehp64dg8UzhjFRRs2Qi4iWcmlSrNh8hEkXLzNkk5bkUVXr2IyatWbOGUD/Xhrndp2SId0FxVouJ1vbNXfyshpcIaUMa+Wr1DMHbplvze02bvGLkHTKMELpMlouGcnxSJOpNaume4gRrhYjZdPSHorZGYak8LkRlxiz15LWdSEmz80acxYye9Fv+2i2XMRas3qqNRQWVzDJGjdaNmlJHjx33Tgsu2okALJax2bUrDV7zgD691KxC9qKLaWURmScXJzSSpQrpJRhqXw9gdOT2xW1M4n/drvDrzMS0eKz82i1ZNFm1kqX0XJJ9sbhqV+OQV2TfEs5UisY62QVo2XTEpCP11NjGWR9ibHCWqKxLmivLbPkkpHswfJrxmD1vInYdv9kXcooq2L4Zsgm0ROHqcN9MRtFRMrL6NqrZq8ltd3wpGBhUTZSLk5pJcpjSCmTqTPjWgkxBkUO8XdXeYB7TWi/e8cbezv1/RXjBqcO9zEtrWG0XOLj3KhqJHMFxlKsIpNVWGDFOUOibLK+xJgtl97J8VTivmoJ5yEpZsmlpjEAX0qCphjA6AS6kCAwUcjMkE1kbKRSo4hIjM64NnstAfrCO1hZlI2Wy46jVcgbnKn/QSbCFVKKFBZXMAuiB5Qnt5bX0SZSGQU6F21mqXQZLZfapgBxjVkSxWp6jh935Q9h4mK04pwhkQnr+oBmy2XWmL66a7UGQwKWrjtEaURhzJSLFkVKsn5vIpu2zWbJJlIuJLWOjc64NnPOZCTH409X5uiyqLOyKBsvFzvVYpGGu+wpIZr8WQTRi8gVR9b6OlL8qQmKQfWy4zj1v2JG8dNzxjJZMmbIpby6CRnJ8gefWjdSViabrl5mzRkp1Mgksj4gC8yWy9ThPt3PYHGQmikXtYqUbP3eZjZmOLNkU17VpOr1RtesNXPOPHXNGN2d4VhZlI2WCy+MzwFgXBD5mkB48sp27RBOl5SggTfOhTd+cz623T9ZNqg+FpFxg+nJXiYyMlouAPD3ncdQ0yj9QC1ZwqysGmbIRgotMpme48fTc8aCRdMnM+WiN95NhMVBapZc3C514QdmJO6YJZs3P/1aVcKK0cXezVxLNU36uyE6Ye9NT/Jg4gCukHJgXBD5vViF0KmckehJLv47FKJX36w1KOCz8hrEuV2yQfVymebR/HCihdlN1Gi5xEJLljArq4YZsomPC7vSItGaOZ2e7GVS1sisOeOC/nJGIiwOUrPkEhKA21ftw/r93xO93ujEHcA82WgpESa3X7PAzP2Xxhpwwt77yBUjHdHLniukFKhsMG5jHBg4PcmjCYXCv6fJ81uPdtzOo2sgrp43EU/PHkf0nDN7JTCNbTJaLpG4EE5SWf7L0ZqzhFlaNYyWTVsQnWJsM5I9WDxjmCbXGssEDaPlkpHs0V3OKJIJ2RlIjo+j8qxIzFxL81fvw/r91qnfG41ZstHyeaP36/mXDGIwsjBmyIWWp4FleJBRcklPtn+bcoArpFSoOckublSKgYFVeKcVCAbDkzoYBN5pZbPom9qCncpJRNZAzB3YGxMH9la8XUbGDY7vn87E/SpipFwiEQBUN7bBl5qI3IG9Nd9UWVo1zJINANQ2BnDHqn3ERfEjYZ2gYaRcFv9sBDVlFAi7rZvagtSeF4lZ8yVsKY3dQMHMVolmyEbr543cr/MGsc3ANloui2fQ8TQAp/fePgzq+hohF97LntNBtHvSCO7FKsPK9Ly+q1y2nIRSj+3ouMHdZTVM3K+RGCmXaLRuCtFla/5z7yX42yfleGQ93Qxqs2QjQHu/adbZ9oBxcqk60YK1Rd/JlutRy993lDONoTRzLcWaK0bMCyWMlE16koeKJVCUGctQByPlkkq5msL0HD96eT2Y+/Iuqs8F2MulvKqR3cMNhFtIKeBLTTR7CEz5sPi4osWCtGizU25xcmixYojdQWa/uBN3vlmE2S/uxMVPbMHJ1nYGIzQPre0yjU7QYMkj67/o+I4vfGyzJotxJMdq1GVf24lYc8VJ8yIWtU0BKq0hRZk5RV6v7yqn/ky7nlGrd6tLfLMqXCGlgHjzdDKLYrQmk4ovjY6lNNPNxhot8UxyZWsq61vw1KbDNIdnGbTGwhmVoGEUFfUtuO31vcRJPFL0z2BTKswqxJorcvNCqRybHRG9CzQUDrFyhRNkFMtQogXS+tJWo7KhVfVl34pwhZQCTrt5SlHbFMDOo8qtyaLjS6PdbbGyGcV40xk5Z9IZtIGozZxWKltj/3uuPFovJZEXnpvzsugOykRIk3ikuD43i2lMttmQNpWIvgjvXJQPX4pzLi9avQtSFBZXYOm6Q51K1iV76SfGGQUtRV0kg2GnRdbY1bobCVdIKSHe1p1sKd2hs1eukptN/PfPRvmxvvgHXe9jNDfnZalOVjGjbI2Z0Og3LV54Fl8+Ajc5RCklTeKRIr6HG/MuymYwKnNRO1eiL8KbvziOlnY2yV5mUlnfrOvv5Twyja32lRUtRV3EzhcZJ3ggeVITRaJ7DX9ZeQLP/LvU7GFRhI7L6NnrxnVp9+dLTcDM0X48v7VM93sYTb6GrjtOuM2SoqUofixoJzSYjZaELwBYVBC+4L34cRnzhEEj0DtXRKXLAaLowtJ1h5AYH6epUoMZjQSMguZeOiE7AxnJHtmmJ1bEhfD5SSPxzWy4hZQykbf1iwafYfZwqJI7gE7ZECk323/uvQRri+jGA+llxbVjiUtaqYX0Nrsgf7DtXGrReoTWovhyFBZXYPlGZ8XY6rH0LCoYji+WXobrJ55DeVTsie47r2eukChddg5xqG1sw29f12ZNd7JHhqZlMM7twtXjzqL2PNawuOybCbeQMsTs0iQ0SUvyYCLFXrmi4i6yo7Ta0AYDsbhqrB8/G9MXPXq4iEpaqSXW3BBvvfMnD8bNFw5AzpKP1H8IA8lI9uDKMWchf7gP4/unY8+x2o4yVjRKHImISocT0WPpie/hxrj+Gfj7zq8pjog9T88dB7fLRWWukChddrYi6ymf5kSPDAvLYGFxBV76uJza81jjS03Ag5cPp1rf2Ey4QsoQpRqddmPZVWxbk1lJGQWAZVePAaAcYqBnI1BTv/WtT62tZPT0xuHhWTkoGNW342e5FC8vkXBLjzxGN+jQg6hMTBygvZFENE5UuqKJTHBSs8bsHl9I2yAgElkDOrOnF0veP2iLc9rlAl779QRcMDjTEZZREa6QMkZOoXG5uva3tQLeOBdag6cH5kvxYslM7R1moou+y1lArHSY/myUH/E9TkezRMcG07L6kSq7Vq83ebI1iNtX7cNzbhfzm7oTlQ5alh4zGnRogZWb0c5KlwvAXflDUHWyhcjKrXYdkHjr3KfOJKsdS7dOysb7n1dQNQgAYWto9N5rF/KHnYmLznVWSCDAFVJDiFZoqk60Yuk6ul14aCEqo2mJHtyYl435kwdpPjSkFrxfZiOxymHqdgGzzzsHwZDQ6XNHhxjQgkTZ7Zduj3qTS94/qCkxRw3lVdZWzuWYNuJMfHSwa/UImsqZXRp0sHIz2jVEKnJP3FFaTaSQqlW+STwy8y7KxgsWSyq9OS8LiwqG477pw6gaBOye/Lax5AcUFlc4xlUvwpOaDCIy2SmTQb9c2tQ3B/Dkxq80dwhRKvouFZhvlcM0JABzX95FpZMOKUr1W8MxTfao1MC6OHNhcQWe3PgVs+ez5NPyOtw6KbtLWTiaCV92aNCxeMawLg0zaGHX7k2/v2xYhzxIazVrsabH6qi3qGA4nr1uXJdEMzMRK5jEqnGtBqdUHKBdg9UKcAupCdjBtaQngD5W0Xep5xrRZ1kNouJMMztcLXa8xbNyqdv9EKltbMMLW8vw9JxxSE+OZ5LwFWkFs6qcMnt5mVrQ5cJgrMz8N/ehR49wuIua2HItxPLIsOznrgaWpYycEIeuNZbY6nALqQnEugVbBa0dQmIteKnnWq3blXgQmHULtasCxuqyZfdDRPwel64rwYTsDCqWHimm5/hxV/4Qqs+kiRGX8eiycleM6Rv7j0wmcp+JZcnUe0GOZW2cOLC3qZZ21qWMnBSH7qTPAnCF1BRidSxyIRzPYxXlTO2kJ3199Ous1u2KZss+tdhRAfOleJkVZ1YzB63ap9uo+ZSVab2YYxqdutQQqXT9Ynw/Q95TD9HzQqpWM6tQh2gizyczoF23OBo7eChJcdJnAbjL3jRIMqzH9kvH7av2mjjKMGonPenrpV4X7VI6fPwkVmw5our9aWPGLdSON98lM0cwc8eSzqnFM4Yho6cXC94qYjIOGrD+bq16SJlVvHviwN5IS/Kgrsna3Xc2lFR2cr+ySqQkYXqOH8/MGYv5q/cZUrs1so4xzTAWKUhqQKcmelDXbN354qTuTJFwhdREYsXzFIzy4zn3OCx5v8SUOp1aJz1p0Xe550ZuxDtKq5kppPFxLrQFY++2ZhzwVlMqFuQPweAze+J3/zzQ5WBPS/Jg2VUjmVpvSOfUr/OyTbFoq4H1d2u1bHMj5ocScW4Xll01Ere9bv7lXolXtpdjQnaGZTKnC0b1xQq4YhpFvD3caG0Pyf7e43YhMT4ODS3tHT/zpXgxe8I5yMpMph5LHQuSON0b87KxXGcCZbLHjR493Khvbpd9TYLHjcevHIU/fHCQ+MLktO5MkXCF1GRi3YIjldYPiyvw2o5jhoxLz6SnGZg/ITsDvhQvKhvo1yl98pqxWLquRLPizBKjlYpkbxwaW4OSv3MBePPTr7Ht/smYluPDzqPV2FFaDUBA7oBMTGQQCxmNmjllNYVMxKj5ZLWGHE/PHoe8wXTaDmtleo4fz103DkveP8hkL6GB1iRSlkzL8cW0LispowAQCAkInFJGaZQTpEEsD+XU4T68+enXqIwRNiW3vhbkD8H8yYMA4HTh/WQvQoKAXWXVAMLnvtgYIj7eTXxhclp3pki4QmoDIpVW2gppWpIHj8wcjvZjezr9XO+kp9XhKM7twpKZI6haN1ITe+Cxq0dheo4fbjeYZbTqwSilIi3JgxsvyFLsDR+d0Zk3KBN5g4xXMEjnVKTsjCTyezJ7PunNNqcx5zq6MVkkCzjycr/9yI9YscVa5dSsmDm9u6yGaqiDWE7wXF9P0xWqWB7KyP03EvHft0gU7Jeqsx39XV40pGtB+9MXJmlvqJEhDWbCFVIbwcLys+yqkZhybibWHwNeueE8VDW1U+1ERKPDkbhYF67p6i5OT/LgkStGIj05HpX1zdh+pArv7P1O9ll3ThmM/zdlcKcyJyxag9JAbmwk8XBSCpEA4K4pg9EeEhBp3fxg//dE47FCXCvpnDKj/I84ZwBYYj6Jslq+4SuisJe0RA+WXT0SQNfxq8XsC50c4uXe6Lnc0xuH1kAQAWVjIgBrrDMR2mPRU06QBUoeSnEPeXTdQQCNHT+PXMs0C/ZH7m2VDS2oOdmKjOR4+FITHa2ERsIVUhuh1mrmjzggo11VkS1BA4GwcjMhOwMeD90MZVqB+eJi3VlajR1HqxDt8gDCpZIe/+hL2We4ALz92Tf4f1MGSz6bdmtQGsiNbUNJZZfvtE8vL4AmPHnNGPxx3ZfECpGeJDQzIJ1T03P8mDy0DyY+uhE1jfIKvNsFVYkbV4zpi7PTE3F+Vm+441yoOtnaZc5YZT7FuV3IG5RJpJA+PXdch+Vbb2c5K1zolDB6Lp+UCYeRwirrDGAzFitaguWYnuPHTwf3xkeFH+Lxq0fhzNTkTmuZduKZmYlsVoArpDZDzvLjT03A4hnDZYtuW+WA1EOc24W8wZmy8Whq6p9GL3orbwRSY5NSVMee3QsfFX6I/GF9cGnOWcTft94kNCuz51itojIKhJXRxTOG4VhNE1FIzCVDz8SsMWcpvsZK84n0+504QDrDOxgS8NK2sph//+efj0ZV42nlHAgnJVpxz2HhbaIR5pCW5LHUOmMZj20lS7ASHUnGI/3UDTacznCF1IZosehZ6YBkhdb6p3YiGBJkv3fR0g2o+75Zd4cxE9LvOrOXF8P7phIppFayYJGg9/sl/fvIi2JhcYXkpdkqVlNaMdopCT3w0MwRqGlsU21FluLGC7Ittc5YxrLbbR1x2MML49sUmr19nYLdXM9qKSyuwIWPbcbsF3fizjeLMPvFnbjwsc0oLK7Q/WzW3WHMQs2cYNlH3Gz0fr9q/l5seRvtrRDb8dKYrzSQ+0xqWHbVKFw57mxk9vLqHk9akqcjM9tK0JBTJHZeRxy2cAspxzE42fUs19dePOSfvW4cppyrL/PdyrG0WlEzJ5xsKQb0f78kf6/U8tZqCS1ARGz60Wrc8cZeVcXQb52UjYJRYUVczSVXztK47KqRlpCJFFrLDzpxHXHYwS2kHMcQqyUrYM+NMNYhD3Tuha0Hp1ne1c4Jp1qKRfR+v7H+Xk0ct1UQE7+WXT2yo3WzEhnJHjwzZywWFZxur0lqXX9mztguc8ufmoDnbDC3xO/+MsJxLsgf4th1xGEDt5ByHIWVyzhphfSQ33Os1rhB2Qi1c8KJlmKjsHMct+w8IegqRGpdn57jx7Qcv63nFqnXYf7kQZg/eZCtPytLlPIBuitcIeU4DqcpFKSHd9VJa3agsQJq50R3SAJkgd3juPXsHXIKbZ+UBCyaMaJT4wY7zy21oS12/qyssHrSn1lwhZTjSOy+6UdCenhn9vSiKuLf/AbeGSfNCavihDhuPfOkk0Jb3wh8sw8f3TUJCd74jtc4YV060RNlFCT5AN1Vflwh5XAsDukhP75/Oj46VXmG38A5ZuD0xDASRIU2EEjB+m/2dfqsTlqXTvNE0UTu0mG3pD+j4Qoph2Nx1B7yGw8dx+2rPuc3cI4pcOuZNE60jHGvQ1eULh2pifGam7d0B7hC6jCc4A7idIXkkBcL4y/78At+A+eYCreedYZbxroHsS4dN+ZlET3Hikl/RsAVUgfhJHcQpyukh3xlQwvkitd09xt4d8fICyu3np1GT1tjjj0guXSsLfqe6FlWTfpjDVdIHYIT3UGcrtA65LvrDbw7wy+s5mHnclgcMkguHdWNbchI9qC2MWDbpD+W8ML4DsDIwukcZ9Bdb+CxCIYE7Citxtqi77CjtNoxa8Yu7Tytit55YfdyWGpw6hqKBell4soxZwFwVvMWWnALqQPg7iBOJL6UBHxd28pv4CpxqgWRVvxid41PpzEvnFAOiwSnriESSC8TfdMS8fSccVi6jif9RcMVUgfA3UGcSBZeNhS3r/pcsme2AGDxjOEdJUi6o4IhhZNDXmhcWAuLK7Dk/ZJT8clhfCkJWDLT2QcorXnRHcphxZLV03PGIT053rH7TaxLh8jSdYfgT03A4hnDkJ7sdaw8tMAVUouhRUnITPYSPdsJ7iBObPKH9ZHMyBdZuq4En39bi/c/r+iWloxonJ4BrefCGgwJWLH5CJZv/KrL7yobWnDb63tt0Yc9GpJ9lva8kKuUkZEcj6Wzcmwnw0hIwsbuWL0XQsQLnLbfKF06oqmob8Edq/bh2evGYdYpFz6HK6SWQsrdkZEcj4dn5aBglPSiFS0XSsRyB4kxPusPVODM1GTL3dS4JS82ooyAsEVs6nAfQiEBt6/a1+W1FfUteH5rWZefO8EaqAWnh7xk9tR2YQ3vLQdR2aDckvaef3xuK2Wd1K1MOi92Hq2G2+Ui2p+m5/gRCgl4YG0xahrDZdqqG9uwdF0J3G7Ydt3FkhWATsooEJad0/YbuUuHFAKABW99jsbWIPqmJfJzDVwhtQxy7o6axjbcvmov5n2Tjd/PGE70N5HEcgcVFlfg0XUHcfdQ4L5396M16LLUzVVPTFJ3UWRFGdWcbMbjE4Cb/vYp0pMT0NIeUvWcaKsPED5oKuubUdPYhoyeXvhS7CFHNd+9k0NeRKVSCakLK8neItLYGsRfN32Fu6aeq2+wBqDGBU/6fd/xxl7UNQc6/i3uT1POzZR8/ztW7XNcaIjWtSFA2cpsxz1cLM+3cnsZlq47pPja5kAQ9/zjcwDOsxhrgSukFkDJ3SHy4sdl+L6uCZeO8OPMXgkY0y8Nv3uvOOaBkZbUA1eNPRupifEIhoROsYMbSirxyvZyeOM6P8Uqm6OW+C3xs20sqcR7Rd91WCEA5QVvx40P6Cwjb9zpn8eyaskhWn3mr9qLXWU1qGls6/IaX0oCZk84B1mZSZaUldIlRqqOK2koS9WJVqwt+s6Sn1kKrRdWkv0omhe3leG/pgyxtExIXfCTh/bBnmO1KCyuJHpupDIKnN6fnpkzWtP7G21t1rv3BUMCqk5o228Aee+DnROk4twuZPYi80yIONFirBaukFoAEncHAKw7cBzrDhxX9ezapna8vL0cL28vhz81ATNH+7vEDkbDanNUs/GRlrKKHJ/UBhaJnCJr141Pi+JAyocKh3FlQ0unmEIryUrpEnPb63uRluRBXVPnS8riGcPgT01QXBNuFzpZO6z0maUgnRtpSR48etVIVa5qKRpbg5i/ai9+lZtlWWWd1AU/8dFNkhcxUsT9c9mHX+Duoerf38jQENK9T27vjrXnkrL9yI+dnr2hpNL2SYZaczaWvH8QvRI8qDrZapvLLy24Qmoi4iL/0KAagHKxg1LQ3hyl42M9p+Jj+3Z5PcmhGDm+f33+Pf5rddd4yUjEzW3J+wc7FFk7Z1drURxYYBVZkVxiIpVRIDz2O1btQ/7wMxVlGV1K0SqfWQ7SuVEbJQ8A+L+D2vajD4sr8WFxpWWVdVK3sh5lVEQAOlUkUPP+RoWGkO59ckrrzNF+vLC1jMqFeMWW0o7/70tJQEt70HBLMm0v2eYv1BmPAHHetGLuS7s6fmbV9cQCrpBSRM2EpnWzZA2NzVE+PjaA21ftw63f1mFRQef42OjNXI7KhhY8su4gXvy4nHg8lQ2tWLH5COZPHqTaCmsFjL7IxMIqmehaFHTxe9506AfVf2eFzyyHmnX73//4HPu/rUfugN5YvfsY1herP0gjERWau/KH4JyMRMvEH5tdZcRKxfFJwwdCIUEy5lWNcUMtsfZ+FpZkJUuxVCxwLNraQ3h5Gx35WP3ySxOukFJCjdtXTcKA2ejdHElch89vLcPos9M7VRKoOUkWk/T05sM48mOj6nEt3/gVBEFQZYU1GqkLzoaSSkteZKyQib6xhCzmTwotzWSs8JnlLsFq1u3J1iCe+Xcpnvl3aewXEyCKUqpUlJnWHtI6kbQJhgR4CN+/d3I8xvdPZz4m0vCBB9bGzlMwC1qW5FiW4uhYYBL+9km5pj1FCqtffmnCFVIKqHH7soz7owmtziGkVqvFa4sxLef0YstIjid6vhZlVOSpTYeJXrehpNJwhUPqgtPT2wMnW9sNHYdajHI3RititY2teHl7uSHvHY3Wz6zXRRgreStWXKwZmJm4oaZOJE3ylm3E3IkDMLBPL1x7Xj8s3yi/71Q3tuHiJ7Z0UtpZJFyShy90DemwCjQsySSWYjEWeHdZDaqa2sN1v12QjfEsLK7Anz/6QvfYosdSUd+CldvLkNnL69jYUq6Q6kRt5qRV4v5IoNE5hHTjq25s62Rp8qUm6npfEkgPpLVF3+P3M4zroiJ3wbG6MgoY426UUsTM3JfFz6w3ZEeNi5DkEjxztJ+ZW1UvZll75IvTe5gpXyfbQvhLRIyky9W1JmckkUo7ACYJl+VVTZr/1mxotlklsRSLIQQ3/e1TtAa7ztfIWuGsvZ92SqzUAldIdULq+li5vQy/zsu2TS3DxPg4fFl5ElOHC7oODTUKSqRsJmRnMD0k1BCtLLNAVGYq65uxdN0hy1vQoxEPifH907GjtJpZ+Sy5DZ+We0wtvhQvJmRnUAnZIXURklyCF605IJmwZAXMDnUQ60TuLK3GjqNVAFw4PzsD976znzh2XQ9KymjHawAsXHMA9U0B6gmXhcUVeFIinMIO0G6zSuM8FmuF/+ZYFv61v8KwvduJsaVcIdVBMCRg+5EfiV67dN0hvLStDNeedw7jUdGhqS2I5Ru/wquflGFZVFkYNahRLCOV1zi3C7NG98WrnxzT9L60YXlQ2SXBTQ7xWJg52o+Ln9jCrHyWFcNdahvbcNebe/Gv/V3jV9WG7MiVC4qG5BJsVWU0EjMv5xtKKjt1oVqxBUjyuE0bjxTRFSFE9MQUWnENqcFH2SpI06PzksEhQ1ZOvNWKtVagjSgsrsCFj23uVK4iFhX14fqNSfFxsV9sEeqaArjt9b0o1JjRHed24eFZOTFf549wwbS1h/Dfbxdh1e5vNL0nC5Z+cFCzDJQQrWV2VUaB8CFxy6RsvLC1rMvnEJUyGrKzYrhLa1CQVEaB8IEhdqIR2/OqcRHKYRcvSyyMznoPhgTsKK3G0n8dxG2v7+3SPKIpoK6zmZlEWpnVYMU1REKSJw5v/OZ8bLt/MlVroJhoZmdVLnIeiHN8bdF32FFa3bHv2AVuIdWA3jiRprYg1fEYgdQtjDRmrmBUX9z6bZ1iPFtBTriDzuYvKvHSx+WWu8HXNAaou0fsbq0AwlnBm+/5KSb/z7+Z1w20qyJWUd+CFZsP4878IVQ+g9nli/RCMwZQjq5Jb+F+8XZUxpRQO5/suoaaAkG4XS7qVkCzEt1os6GkEvXNbbZs8BIJV0hV4gQlQgvRMV9quxstKhiO0Wen44G1xZ0KT7td4fg/sZuUlYnVd1ktdrVWRFLd2IZVu44Z0oHGzorY8o2Hca6vF5XPYFb5IloIoBcDKIXdQ2DUoHY+2XkNsVKm5RLd7KSgvvXpN3hF4vy0W5wpd9mrxAlKhFbEDUHOzRzLPVswyo9Pf5+P1fMm4qa8LADmJaNoRYubTA67Wiui+fgwWRy13s87ITsDaUkeXc8wk4f+VYLx/dMVXYQuhDvVKCFadcTXc07jhBAYEW8P+ePZhc5hTqTY2UX9VeUJZm7o6Tl+bLt/MlbPm4ibT51NdjqaGmW8rmLY0O/eO4C2duuHpHCFVCVOUSK0cGavBOIe83KbRpzbhQnZGYq90q1OZX0zlefY2VoRyfbSaqLX6f28G0oqZRM97EBFfQv2HKuVVSbFfy+8TCGj6RSiVceXas85pLRHaMVp3qtWBQVCq5XZzpeZp/9ditkv7sSFj21WHZNOElspnk3rbXw2yVHTGMDERzcxyYOgCVdIVeIUJUINkbdx0jJXSlZEu1uZafS6BuxtrYgkEIytAvROjtcVM9jWHsLv3jug+e+twg8nWmSVSV9qAp69bhzyh/UhepZo1Xnj5vMx/5KBuHJMXxZDZgJNT4OI3fcVNaQleTB1uE/T39r9MqM2UVJMQJ794k7c+WaRolLr5DlU09hGLcGUFTyGVCV2j99SS3TdN1ILsdLr7G5lzujppfIcpwTUkzBrTF/NMYOFxRX43XvFlqhJqxfxQivWwpRKCgwEyD+nVVvJkrDtyI9U69TafV9RQ11TQFdMtjj/PjlShXl//wwtNqswQJooqaaLItA95pCVy0RxC6lK7Ozy0IJotREXLamFWOl1drcyx4rxU4PdrRWkaLXmiAcKLau0mUTH/MW5Xcgd2BuzxpyF3IG9JQ8IJVej3eMln95SigsouhHtvq+oJZbyFMtN/VFxBW5ftcdWyqgIiSdOS3iZ0+eQ1nJhRsEtpBqQy8pzEmlJHjw9exwmRh2U4/unIyM5XlZBICnpMiE7A74Ub5c6gHZASyJBLCKtZWKnJicoYCIZyR6M75+u+u+cFhOoNuZv46Hj+OO6L2V71TtBNsdPtOK21/fiOQpZwN3Ne6WkPMWqgvLo+hLLtpVVg5JSria8TLQ0j++fjvQkjy0aS+jBqpZgbiHVSGRW3lPXjsHiGcPMHhJV6poCcLs7130rLK7AxU9sUVRGgdgHb5zbhdkT7NGxKprFM4YxcXWI1jJfaqKjlFEgHFB/8RNbVFvCnBTPtSB/sGqFa8FbRbKVLFZsPuwY2QDAPW9/rjvJSfReOV0ZjZVhH6sKyiPrnKGMAspKudrwMvF8c7oyCljXEswVUh1Eutz8DnS5Ri5oEvdgtHtfiazMZCpjNJql6w4xCQoX3WsfWjjgXA8kiQjRLkYj+oobgT81AfMnDyZ+vaiYKbkaX7V4zV61NLYF8cmRKt3PmTrcZ+vSYCQoZdjHclMLAF782P7KKEnZKzXhZXYPf1EDCy8fLbjLngLBkICl6w4Z+p5PYA6u8gAuFyAIwJoAcC9WUX2PqhOtHYdjLPdgRrIH/7n3EsQr1M6LhNUNjbVcKupbcNvre3FzXhYmD+0DuICqk62KnapiYVQhbyPmjByxEhGkZJCRbIxiwVouJ1ra8VFxJQpGkVlI9xyrVfy9AKCumb0Vx+j58u7eb3HRkDN0PWN3WY0hpcHMXEtKmO1VMFIusTxxsUI4xPCy8f3TcfETW5ha1q00X6497xxLJjQB3EJKBaM3gVLPHPzcC8TFAW53+H9/7g3/nCZL1x3ChY9txorNR2J+vprGQMyDNJJwHCldpdQouQDhzlJzX96FuS/tillKRAmjbuZGykYOuYB6ORkYkVVvhFxOtrbj9lV78ej6EqLXV50ki61OS/QwS6w0Y76UVZ3U/QxaNYKVMHstiRc7qRAHM2MDjZKL2wU8PSe2J04pATkyvGzPsVqm+6/Z8yWarMwkU96XBK6QUsDITaDUMwdumW/N7aY/ySvrW7B841dEryWRg+iW/WD/98gbpL2NZDRGy0UKtfXxjErasYJsIomcJ2YmLhktl+e3lmH9/u9jvi6TsKzYjXnZeockiVnz5eiPjbriSAuLK5h7qqywlpQypc2KDTRSLiEBSE+OJ3ptrJq/03P8TM9vK8yXaKwaPwpwhZQKRn3BT+D05HZFXfnEf7vd4dfRQs3xEEsO0QWK3937nb7BncIMuUhB0qkqEiMs61aRTSSR84RUBj29cVTHYJZcHlhbHHNuiBUJlNqLhuNSB+HZ68Yhg/BwJsHM+XKiNai5HI0R5cGstpakFCkzmm2YIZfK+uaYnZdEohOQV8+biG33T1ZdylAtVpsvWtvNGglXSCkwITsDvRLoHphSiDEo0ZNbRPzdVQbH9JNMdJauaSvJRU2dNyMs61aSjdQ8IZXBhRSt6YB5cqlpDMScG5HxXUquRvF1HorxYGbPFy0ud6Os7GbLJhopRYrETU0bM+SydN0hos5LIko1f1kp8VaaL6QVcMyGK6QUiHO78PNxZzN/H7mJrfV1msYg82+lic76wLCCXKIhUbSMsKxbRTZy84RUBjuP0i3kbKZcSJWu5deMUXQ1ipe84yfo1fM1e75osXAaFcNvtmw6ng9lA4CSm/qZOWOpVyEwQy7R80RtuFQkrJrdWGW+AOoq4JgJz7KnxKUj/Hj1k2NM30Mg1OhIX6eF1CRPpyxWX0SxZTlYHxhWkEs0JIrWhOwMxSYDNLCKbOTmCUkmbHqyh3qCk5lyqTpJ9n3nD+uDS3POkmwvyuqSZ/Z80dKW16gYfrNl0/F8xLZ0KbWmdbtduO31vfTGYwG5qGknKoWoxIdbFNPZj82Wi9sF3JDbH5eO8FNt0csSbiGlhHiwsmRNIDx55Saw+Ls1DJOTEz1xeOPm8yVjceRgfWBYQS4iauJ04twuPDwrh+l4zJbNr3L7K84TEhfjlWPOoj4uM+WyYssRYkuOnKuR1SXP7PmipfKGUTH8ZstGLXJzZ3qOH89dNw6+FPXKvxRWkYvetpjTc/yYNZqeBdFsuYQEYOUnx1Df3GYLZRTgCik1xIPVBXaxOvdiFUKn2g5HT3Lx36EQ2/pmFfUt+OxYjWL/7WhYHxhWkAugLU6nYJQft05iky0NmC+by3L8MedJrEzY/OE+6uMyUy71zQHN7kWRDSWVFEd0GjPl4nZBU4tZoxJ5zF5LIkpln0iZnuPH9oVTsHreRNyUl6VrPFaRi4gWA0gwJGD7kSq8vedbauOwilz0zhUjYaqQ1tTUYO7cuUhJSUFaWhpuvvlmnDypXGvupz/9KVwuV6f/brvtNpbDpIbcwUqTgYHTkzyaUCj8e9Ys33hY1WHKouZoNFaQi9Y4nUUFw/HMnLHMCsGbKZvaRrL4RqVM2PD8oWPNicTsOaP1oCgsrsArDDs1mSWXkBC7KYAUrGIApTB7zgD6LYEicW4XJmRn4MNi/ZcbK8hFRK0BRKz+MvelXWhsDVIdi9lyoTVXjIKpQjp37lwcPHgQGzZswAcffICtW7filltuifl38+bNQ0VFRcd/jz/+OMthUkU8WN/4zflIS2SnYLzTCgSD4UkdDALvtBq76NUcphtKKtHQwt6PZYZcfl8wTFX4ghwFo/ri099Pxep5E3HHJQMpj9K8ObN03SHieSLnYoxzu7Bk5ggm4zNLLloPimBIwMI1B9gMKgKz5KI1vMcIY4CIFfZfQF8olGgR/O+3i6iFfpgtFy1ljYxoTGK2XABzGyaogVlS06FDh1BYWIhPP/0UP/nJTwAAf/3rX1FQUIA///nP6Nu3r+zfJiUlweej76YzgmBIwO6yGuworWLa3u9erMK9JsYqiYdp7kDlcjyFxRVUA+hjYaRc/KkJuOnCbGrxOaJCxmrzMGPOkM6TWEzPCYc2PL+Vfh9uM9eS2u9659FqQ1pjAubIRWt4TzAkIDUxHvdNH4qak60dyYKsCuWbvf8C2mVVWFyBhWsOMJlHZslFS7iUkU05zJ4vVi6GHwkzhXTHjh1IS0vrUEYBID8/H263G7t27cKVV14p+7dvvPEGXn/9dfh8Plx++eVYvHgxkpKk2121traitfW0W7ChoQEAEAgEEAgYOwM2HjqOZR9+gcqG8CFDuZY3M7xuodP/kvJDfSMCgRTZ3wdDAh5ddxDeOHvEr6jlDzPORSjYjhBdLw8yk3pokpnW75E1seYJCcGQgMID3ztuLmUm9eiyT4n/ltq/dh35wXEyEElL9GDs2b1U79vR+y4QTo66b9pQ+Hp5UNvEroqFEizXoy8lQbOs7nqrKDwum5xPJPhSErDwsqGYcm4msUx2l9Wg5mRzTDlYdV8lwQWgj8a5Qgs17+sSBDZFB/70pz/hb3/7G7788stOPz/zzDPx0EMP4be//a3k373wwgvo378/+vbti/379+P+++/HhAkTsGbNGsnXL1myBA899FCXn69atUpWieVwOBwOh8PhsKWpqQlz5sxBfX09UlKUDROqLaQLFy7EY489pviaQ4e0u0kiY0xHjhwJv9+PKVOmoLS0FAMHdo2tW7RoEe6+++6Ofzc0NKBfv3649NJLY354WgRDAqY9ubXTDd1OeN0Clv4khMWfudEaInN3+FIS8NFdkxTdI+sPVOC+d/cTjyM9KR6zz+uHldvL0dRO2exImcevHoWCkWyKDO8uq8FNf/tU9d9p+R5ZIt7OY80TEtTOJTvw5DVjkD+sT5efBwIBbNiwAVOnToXH0zkOfWdpNX7z98+MGqKhqJ0vsfZdF8J7So2JFlLa69HtAv788zG4dETXeRMLrfuKFXG7gM9+PxXxPbSnwZDKw2r7KilpiR4smTlCco8xEtFrTYJqhfSee+7Br3/9a8XXDBgwAD6fDz/88EOnn7e3t6OmpkZVfOj5558PADhy5IikQur1euH1ds3A9Xg8XTZzVnxWWo1jta1gn+PJltaQC63B2J/BBWDRjBFI8Cr30D4zNZnoeSLHTwTw5Oaj+NkoH/61n01ZG1qcmZrMbH5VNbWrkls0pN8ja0jnCQlq55KVcbuAFbPH4bJRyhcaqT3sgiF9kOiNNyyO1GiO1bZi37cniGKOSfbdihMB9ErogRMt5l1waa7HZ+aMRcEo+fwLJfTuK1bi1knZSE7UV3lj4qAzkdEzUbYpRzRW2VdJ+d9rxiNvcKbZw1B1TqpWSM844wycccYZMV+Xm5uLuro67NmzB+PHjwcAbN68GaFQqEPJJKGoqAgA4Pdbt+WVXTLYaOAn6MwkIpZ7IrUci902Pi2vRVJ8HJrarGcldSFc3klNJqda7BKAHotbJmVTa1UXq6OTnVgxeywKYiijcsS5XVh21UhDEwWNhnQ/JX3d+HPS8e+vqvQMyTBcCO+DaVEd8dTsu3LYbV/xxLkQCHZe7W4XMO+ibCwqGK77+WK5sN++vrdD7k7Bl+LFRJ2JpGbALKlp2LBhmD59OubNm4fnnnsOgUAA8+fPx7XXXtuRYf/dd99hypQpeO211zBhwgSUlpZi1apVKCgoQO/evbF//34sWLAAkyZNwqhRo1gNVTekC33+JYPgiXPhuf+UojkgU5zMolw38RzMGNlXVQuyOLcLsyecg+UbvyJ+HwFAZUMrfj7uLLyz9zuNo2WLmkxOLThF+Xr/8wrcN30YFVk54fCgoVQApzvtPLi2GMdPmOOOZgnpfkr6uosGn2EbhVRssSvX9lMPag0EZnNTXhbuuXQo/r6jHMdqmtA/IwnX52bpctNHI5YLe+hfJUxLPxnN7Ann2KY7UyRMe9m/8cYbmD9/PqZMmQK3242rr74af/nLXzp+HwgE8OWXX6KpqQkAEB8fj40bN+LJJ59EY2Mj+vXrh6uvvhoPPPAAy2HqhqQfty81AQumDgEAvLq93HYK6YyRfTWV7snK1JZYljf4DGz84gfLuSbvyh9CzeoHnC4TFn3wiMqXnaFV8knEbodHWpIHT88eh6rGVmpKhcj0HD96JXgw96VdVJ5nBdR6H0j33etzs/DStjLLXvCSvXF4+IqR8KV0niO01o1InNuFWWP8TEqnseCFrWUYe046br5oANP3mZ7j73QBKK9qxOrdX6OygayphxXJykw2ewiaYKqQZmRkYNUq+eKvWVlZiEzy79evH/7zn/+wHBITlKw30fXRth9mW5+UBRnJHlQ2tGBHabXqQ1Wrm8iXkoA/XZGD21ft0/T3rNCqYEtRWFzRRbmKtKI9e904/O69A6hptNd8iYR2OIt4eCzf8CVWbCml+mzaiJepWWPOYvL8qpP2PTCj0VJHknTfje/htrR1vbE1iESPm7oCGk0wJOD9z7W3qzUaAeEGLFOH+5hb+8Qa0CLzJw/uUFA3l1QAsKa3Tg67hWeI8F72lIjVj1u0qu04ag/XUSQ1jQEseKsIs1/ciQsf26y6baiaPtOR3TZSk/Qnw9CG1kKX6xBSWd/S0ed8eo4fi3/GpkORUbDYGOPcLuQNih3HbgXm/f0zPLXxMJNe0nY9dKTQ2naXdN81spOTWmj0pidhd1kNkWdh2vAzkOCxhmpgVtvLyK5xf/7FaABAz3jrF27V0q3KSjC1kHY3ok3/0m46+8V1RCIqTKSHh5rYv2gryY7SahpDpgatha7UIURM7BItA74U6x2gJLBO/rJLPFxTWxDLN36FVz8pw7KrRlIN93BCrPH8SwYhb1CmrnAGsn236+syk724Y9Ve0z1WkW1kWVpJSb0Vu8rq0GJASJm3hwut7bFnrlXW+PaFU7Dn64ZTRiUXzuufjrveLkKtwWFlEwdkYOfRrkq6Fi+D1bDGNchByPXjFmHtlqGFS2Y+i9uHmhs9qXWiq5XEOsesC/QWeixLReQBNSE7AxnJ1rMUK2HExigmzNmFuqYAbjtl+aaFeNkD7HfNFS05C6YOkdwn1RJr35V6ndvtMl0ZjYR1tRZSi7qcTC7LodfOuyCnD/770qFEr62xSGhKnNuFvMGZ+O9pQ/Hf087FxUPPxA25WYaOwQXgWHUTnpkzDv4YXgE7whVSg5k4oDfSkoypj0pC9LYt/lupf1ekwkTK9Bw/Fs9QLtWxeEbnDOTcAebXUAOA9CQP1YWupqxNnNuFh2flUHlfozBqY6QZz2sUtF2z4mUv1UJ7SiysYsmxWrk+1iEYasOnInEBVF3n1+dmI7Mn2UXbqhfywuIKrPyk3ND3FM/e9OR4bLt/MlbPm4inrh2D1fMmYtv9k22tjALcZW84VqojeOukbLz/eQVqTjZ3/MyXmoDLcnx4ZXt5zL9Xs6EHQwKWriuR/b0LwNJ1JZiWczqAfeLA3l3q8bHE7QIidYW0RA9uzMvC/MmDqR6casvaFIzy49Zvsy2THetPTcDiGcOQnuwNuz57egEBTLLJlbBjDCUr16zVqlEo4aNU/kovRs2f0X1T8E1DIGY1ANZxf3pKpwkAqhvbkH5qP9Z7papsaIEvNZHotaSvMxIxB8AsH55orLCLx5UUrpCagFhHcMn7JVTjY+LjXJg7oR/e3vsdGlvli8q7EC5avqhgOO6bPgw7j/yAqkM78coN52HioDOxu6yGSCFVs6GrcVOLi8wo5V1UnVbMDluawrGrAnIHZGIiBXdiNKTlaiIPqPumD8PrO4+hsc2ccmF3ThmMAWckG6pwxoJGDGWcGwgSiDTZG4deXg+V9UrTMifGI1uZ//3FaPjTEqnW1KTBhOwMpCZ6UM/Ybf9fU4agsV0gqsLCGrnSaWmJHqLwhdb2UEecux5lrOZkK2aO7gt/aoLiuWDFBB2lHACjsONlnATusjeJ6Tl+bL3vEmQk03O1JXl7YOWOrxWVUSC8kbywtQyFxRWIc7s6Frx4UMRy7WjJ5NPafUVU3n0p+trEKSG6mN1u4L//8TlWbDmCFVtKMfflXaqrCpCgFPsnd0DtLqvRpYxOGpyJ6yZqj7l8+7Nv4O3hphLvRws9MZSJnjikJXqIlFEAeOLqUdi+sLOL7Jk5Y7vEcZFA8zAhzZyOxOhvz5+WSBTfaTRxbhduystm/j7nZWcQVwMwguk5/i7u3qfnjiP6W7F7nt4QkYzk+I71q3TOmB3WIYWWNSdFamIP3DllsKrEVbtn0ceCK6QmsudYLdUak2pdKXLxbFoUpljo6b4yPceP7QunYPW8ifhVbn/i9yRh8Yxh2Hb/ZACIWYaJJmoPKL1Wtf3f1uOyEdoPPVZy0IvWcj7NgSBxQsutk7JRMKpvl8SZglF9Ow72m/KyYj6HxWGiZV74UhPw3HXjmF/07HB4zp88iHlMv7hPSimCZsX9Rc/liQN6E8eXugAk9HDj9wXDNL+/6IYX12/0xc5v4QQdGh6OBflDsHfxpRjm74WWdnUtsq2opNOCu+xNxMyg+kgX+U/OSenyeznXjtb4Ly1u6kgi42Ve23FM1XsrkdkrfCCTlmGiuRGQlqsB9FvV6poDgCvc41hLBxKWctBLpBxf21GOD4srqTy3d3I8ls7KUew7L87L3IG9MSE7AwvXHJCM52TlmiWdF4tnDENmL2+XOdalDNLqvVTiUa2SuBQLVmFBvhQvHvzZULSV7enyflaM+1PTHU5s76w11CH6kqJmH7QCevbiyMYnWuJQb5mUbUklnRZcITURNRO7d3I8qhvp960OK8VdFVKA7kahppuVErRrL57ZK0FTfCstSA8o8XPrcRVVnWzF7AnnYPnGw5r+3qh6iVoQ5TghOwPjH96gW6laPGMYfp2XrWqui+tlxebDeHV7eScLLKtEHtKLntxniZ5/y64aSSVZwyqJSyRMz/HjmTnjMH/1XsQqgJAUH9fhthZJT/LgkStGIj05vtM+GQq2Y7018hCJEI0QC989QOg9UD9L5NzwVlXUpSA9g8RzbkH+YGRldo6/1xKH6gLw/ucVuG/6MMsq63rhCqmJTMjOiJlFnhQfhxd/9ROcl5WBi5/YQr0IdiylmOZGQcPqKiq2ei0akRbZD/Z/T/Q3Zlq0IxV6rd8/rdhFq5XLiUTJ4qUmESOzl1fzxevO/CGdWg+ytPjQuuiJyK1RNWhR5s2mYJQfKzBWsVXxrZOyw0mgpdUdxdFFd7fUZw2p88Raguk5fvTyejD35V0xX5s7IBPv7PmOONHPb6NLihKk1QqUzjUtcahWNgjQgiukFsfbw92x4SkdPAKAtCQP6gnjSCMVslCwncXQJaFhdZ063KerHFT0Qa0nvtVI5JSFjGQPWgJBBIPSJ2Dkd02jlqDZcoiFmAgndfG59rx+RBZivZ/RSIsP7fCayDW6/UgVVmw5QvR3sayxVqdgVF8853ZJrq+HZ+WgYFRfAEDe4EzkDbZGjWQWTBzYm8jqPnFgbyyZGds4cFNeFqYO91naDa8Wpb34yjFnIT/G59VzqbeyQUAvXCE1kd1lNTGVqtqmQMeNKNbBA0BTi06jb/J6D2sSuSkRfVDrjW81EjmFfkNJJe5cvafL66O/az0hD1aSQyzk5AQAb376jS2+azXQjsOLDIF4d++3RO5JwPoxo7GwWzwjC9RY3cXLn1TsdFqSh3q7XCuhZ67oufBa3SCgB66QmoiWUkixFoGUwhpd8N1O8V1SaLkhKt1cabs9WSOl0E/P8ePJa8Z0SaKI/q5JLO3R/1/8N2AtOcRC7uJjp+9aDSyssjTck3bDTvGMrFBjdRfPpJ1Hq5nXcLYaWueKFsOAXS/LauAKqYlodRUrLQIphXV8/3TsOVbrmBu/3sxiKWi7Pc0gf1gfrC8DXrnhPFQ1tct+bhJLu53lEAsnfNdGotc9ybEnaiyAcW4X8gZlIm+Qc0MZaKK2a5bdL8ukcIXURFi5iqUUVifd+PVmFsvhFHfdhOwMeDzKtRVjfVYnyEEJp3zXRsHl1T3h1mJ2yHbNOlUXNzIEortclrlCaiJ2cxVbBZZy604bsNJn7Q5y6A6fkSZcXhwOXZRi3bvj5Y8rpCbD3Yfa4HLjcDgcjt2Ru+h1x8sfV0gtAHeHaYPLjcPpSjAk8DXB4XBsB1dILQJ3h2mDy43DOU1hcUUXr4FTCpJzOBxn4zZ7ABx1BEMCdpRWY23Rd9hRWo1grF53HA6nWyD2xo7uAFNZ34Lfvr4XhcUVJo2Mw+FwYsMtpDaCWz84HI4USr2xBYST/R76VwmmDvdx9z2Hw7Ek3EJqE7q79YNbhjmscMLcitUbO7IPNofD4VgRbiG1Ad3d+sEtw/rgSS7yqJlbVpYjafeyD09dXK00dg6HwwG4QmoqpAecGuuH0xJ8RMtwtDJeUd+C217fi2fmjEPBqO6rlEbOocykrsuZK/PyyM0t0evw7HXjOmRkdTmSdi97bccxvLbjGLOxW1lp53A41oYrpCah5oDT0vPeCShZhpWJLTAAAFUESURBVEXmr96LFRiLglF9DRuXVYieQ944AY9PADYeOo7LRp2tSuHqbqjxOmwoqbS8HNX2xmYxdqk9LS3RgxvzsjF/8iCumHI4MejuFzoeQ2oCauNBtfa8tzuxLMMAEBKA21ftc3wMbTRycwgAFrxVhPX7v1dUuICwwmXHeEka7CytJvI67CyttoUcxe5lwOluZUrQHrvcfKxrDmD5xq8w/uENjlujTog9VsLpn89qFBZX4MLHNmP2iztx55tFmP3iTlz42GbHrRsluIXUYLTEg5JYP9ISPQgJAoIhwTE3qsoGcouvk2NooyGxHD+wthg1jQHZ30eGeUzIzuhWt/LC4gosfPcA0Wt3HK2yTbiMXPcyOfSOXbTmVDa0YOkHBxXnY11TALe9vhfPWcCaTAOrh3DoheTzSVnzgO7Z8lIv6/dX4PZVe7v83EpeGCPgCqnBaIkHVerdLlLXHMDcl3Y5ZlMsLK7A0g8OEr9etGblDc5kOCprQDKHlJTRSDaWVOLut4sce7BGIxfGIA/ZYWqVcJnI7mUfFlfgtR3HYv6NlrFLKSwkOOHiSBoKI1oU1x+owJmpybZRzkji9t1uSIRn9EB7CDjZ2t7xs4zkeDw8K6dbx/nHYv3+7zF/9T7J33WHpOVIuMveYLTGg4rWD1+qslu+8tSm8dTGr2zrahE3RFKlSuSOVc4vfwXQVX5e3l7u+FJiouvxvb3f4nfvFRMpoy6EFXNSy6GVwmXE7mWXEV4o5MYu57JVCheJhd1LT8XycAFh5WH9/gpMe3IrAOC+d/fbxv1K4n25Y9Ve3CYZntHeSRkFgJrGNty+ai8eXV+i+J7dNTSgsLgCt6/aB6WP3BE+dLTasHGZBbeQGgzpwVVe1djlZ6L1Y2dpNe5YtRd1zV0VNnFeL994uONndrJ4kWyIctQ1B7qFe4OG8uMC4HJBciOMvpUD9nXDabXkAcCDlw/HxAG9FcNlXAB8qafdlVZCDPWJ9dn/72DXUlBScstI9mDJjBFYsk7b+hSxijVZC6QerttX7YU3rrOURGPBTXlZmDrcZ8l1RBK3r+W7f35rGUafndYl+XT9/u+7hBfZ6bySgyQ5qa09hHv+8TnxM+94Yy+WXT3S1nKJBVdIDWZCdgZ8KQkx4yNX7TqG+ZMHd5nEcW4X3G6XpDIqh53iUEg2RCUEAEveP4heCR5UnWxFZk8vIABVja22U6bkUJtRHY0Y9iEQ3MpXbD6M1bu/6TRffSkJWDLT+geGevf8aeLj3DhU0YCpw30d4TJSCABmjvZbck7FuV1YPGMYbl8l7Q4UefWTY3j1k2PwpXgxe8I5qG8O4JXt5V1eV9MYwP97u0j3uKxkTY6ERInQo0yL8/CV7eV4ZXs5MpLjccWYvpZSTtXE7avl7n98joPfN+CCgZmYOLA3Hi88hOe3lnV53enQAHtWTyGJvy0srsDv3itGY2uQ+LndweDCFVKDiXO7MHvCOVi+8SvF1x0/0YYVm4/gzvzBHT8TN8wPVbp97BSHQsN6UtnQirkv7ZL8nd1v3+IcKMjx4WUJpYGEtCQPrhx7lqTSEU2kpV2ksqFFNkHFKmVL9FjaAaA1GMJTm47gxY/L8L+/HI1bJmVLHp4A8MLWMow9J92Scyo92Uv82sqGVsnvmxZWtiaTJinRVKZrGts6lFOr7Es1J1uZPbslEMLT/y7F0/8uRVJ8HJralJWx+av3YQVctoo/JYkvBqD5ogzY4xzXCldITSArM4nodcs3foVzfT0xdbgPKzYfxivby1Df3B77DyWwUjawEqytJ5Ebg5j8YbbyRIrUoelyKVs6pahtCqBBhYVdjkVrDnTaGK2UeazX0i7S1BbEba/vRbI3TvF1Vj0krOIeF6Xy4OXDLScjEiVCDJX6pLQKyd44VZYtEios4MUKhgRUN7JTSCOJpYwCYkm/vVjww2BJb6HVIIkvvv+d/QhBW9iD+JyK+hYs3/AV8gZlWv7MUgtXSE0gU4XV4u63P4fb9TlOUtoArXJAyaHXHR0L8ZkL3ipCfI841DfbI3ZJ7tBUq4yKvLP3O91jqm0KYOfRauQNyrRcEX7a81xJAbHqZS8YElB1whgFIxY+C6wtuTJFscrwLVpzAPe/u1+zMUAND/2rBJOH9sGeY7WGXpT1xFqzZvnGcNiQWWFCpNUSSC7B9S105tCKLUewYssRS59ZWuAKqQmEVGgRJDdJNVTWt+DFrUfxTW0T+mck4frcLMLCNmyJPCyuPa8fU9chADQHQmgOhDr9zKqxtnrdzyzZfqQKEwf0Vl1blxXiPDp8/CTT95HCSpc9KykYvZPj8Z97L0F8D/OKushZ7689r1/MJKXaJv3eBBLEi83ERzehprGt4+eslQ49sdZGUdlgzt5cWFyBR9cdxN1Dw9USWoOujljrc3on44eGFpR834DmQDu8HuPnt1XPLK1whdRg1BTlZsGjH37R6d+PrD+EWy88B0NNGg8gfVikJvZAQ0u7ZgugFiLLtkQrT2bGRtJyP7Pg+7pm4q5HrC2IZithYriJ2XG0VlMwqhvbsOdYrWnWYyXrPeuLrxYilVGAndIRDAnYebQaC989YJm5ooQAY0NjxHkTH10tgXGstRrslB9CAldIDcRqBwUQjtN55ZNjeHyCOe8vJxMj3GNyiNnld+YPAWB+bKSVLG/RtASCuEOiw4gULD+H2WsrLcmDCdkZps8Vq1rTzZrDJHF9VoeF0mH25U0rRoXGWHUdSXE6rvRL5A06w9ZxpbwwvkHYYYK3tYdiv4giVpbJ8o2HUVhcIVsE3Iji8WLB6MPHTzB7D70UHjxOXIKMVcKaFeZRSyCIdUXfSRYMN7LRQCxrtVlEfvdGFkK3sndBDZFeBr3oaWxgBYy43Nhx3qzYUmqbBgxycAupQdhhgr/16de4adLg2C+khNVlcuebRUhPijclNtKuFgwpWJf7scI8agmE8P/eli5ybZRbzexwIDkykj0d3z1rC3J0uATLuppmsLGkUpd10AqXN70YUcfWyl6pWNg5rpQrpAZhhwn+dW2zoe9ndZm0tocUDzRWsZFmu59ZwLLcz8aSSibPpQnrOForz5mx/dIQ53Yxr8Qg112KhPgeLrS1W1F6nXl5eznOy87QLCcrXN704ALQ3h5CMCQwdUtbtXkDCXaOK+UuewOwUvkVJb6ubjS0n7CdF30kNBVrJ1gwIklL9DC9qQdDAt4r0l/Cyii2H6mivrasPmc2ffEj1u+vIOoBr1U2cm7oyJaUSthBGRXRIyerGwFiIQC4/tXdzN3SYvlB+6hynaEZ4mEkXCFlTGFxBS58bDOWrjtk9lBisr20Gne+WYTZL+5E3jL2cSi1jW2w0eVNFpqKtd0tGNE8PZet22h3WQ2x0mEFVmw5Qv0wtcOceeCfB4grMajF6go5bfQoGk4xArCOy45zu/Dg5cOZPNtI7HYB4QopQ+wcPC62h2S14AuLK3DHqr0wwBDLDBfC8W80YyPttoHIIcpm4gC22bB2lBftw9QOMqghrOWp5bPYQSGnjdbv3O6WPxEaVvVYTM/x4+k5Y20tK7tdQLhCygin3NoXrTmga8FLZdQ6QTasWiHabQORgpVsoudSW3vIFqEw0dA+TJ0wZ0S0fBY7KOS00fqdO8XyB7B3SwdDAjZ/8aMtzykWxhIj4ElNjHDKrT2yPaRatHZHsQOsWiFOyM5ARrLHVm7oaPqkeLFk5giqspGaSy6X9tapZkMzySk8Z+K7FFS3E3oqMThJISdBr6IxPcePZ68bh9+9d8DW+4wIiwtJYXEFlrx/EJUNrfDGUX88U1gZBIyAW0gZ4aRb+ydHqlT/jVL9Tqt0udBKRnI8Hr96FFrbQ1QSwCItf7vLanD5qL6URmoO//PLMdSVUam5ZFdlNBIa+4QTrF4CtB+gTnFDk0JD0Zie48fORfnISI6nNCrzoH0hEfebygb7eV+A8MXOjiWfAG4hZYaTbu3f1akrB+WE7ihK1DS24fpXdnf8W08dRSnLX68Eey/LqpP0NnInhHcoQWuf+MGmh6dIWpIHU4f7NP2tqJD/9nWyjmF2xQXg6Tn0FI34Hm786cqcDrnZdY1VUwzbsfN+M2XoGfjNRQN5pyZOV5x0a++blqjq9U4JVyBFa5KKnOXvRIt5bVNpUF7VSO1ZTp1LtGO8Pi2vpvIcs6hrCmDl9jLNZedEN7QvxZ6GgOzeSTFf8/ScsSgYRdfqFU7cGYd0G1tK//CvYmqJTXbebzZ/8SPqm9tsq4wCXCFlRqQbzb7TI4za+FGndUeJBUmSilRCjl1v4rEQ267SwM6hL+dnp8OFruufRYxXUry9reoAsHTdoY6yc1pKY03P8WP7wslYkG9ctzlalFU3IT7OhQRP1yM5LcmD564bhwIGoTyFxRVYuq7E1vHHNY0BaolNdt5vALZVB4zA/ruYhRFv7SxaQD6BObjKczqxY00AuBerqL4HAKQneVSX7qmh6LJVi1FyiUYpSUWug4zRCQVGyoZWlxAjQl9YyeXvN0/ExpJKPLC2uNN3zSIh7upxZ+OfRd9Tex5g3loCtHdvinO7cGf+EJzr64WF7x5AXTObNcZCNm1BAQgKSPbGYdLgMzDwjGTkDsjExIG9O9ZRdGtU0T0r93MlWHT3MmvO0FIkWe03RsiFdTc4I+AKKQOiN4f/3HsJ/r6jnFpx/FLPHLjd4ckt8nM3cFVoDgYG6E7yvEGZqpUKswLljZSLHNEbo9ymb7QyarRsaGaQ+1MTUFnfwsSazEouPdwurNh0GK/v/rrTd52RHI/FM+hXZ7hgUCaS4t1oagtReZ7Za0lv+8PpOX70SvBg7ku7qI+NtWwaW4MoLK7Es9eNw8SBvTvOkvKqRqze/XWnZBt/agJmjvbj/c8rulQzUbr0sIiVNHPO0FIkWew3RsvFzlZe7rKnjNiZafaLOzvcTxc/sUV1YpAc4uSWwu0O/54m2w6rb3XoS1UXc0oDo+UiR+TGaJUAebNkQxq6IVWrVkQMfWGpjEqhVy7tIQF/2XKkiyu0prENd6yi33Aizu3CrZMGUnmWVdaS3jqTEwf0RloiWS97UoySjQBg4ZoDyFt2+ixZvvFwl8zvivoWPL+1TLKaiVJcO+1YSTPnDM1YbNoVK8yQS2ayl/ozjYIrpBRRKnX0yvZy3c9/AqcntyvKYCD+2+0Ov44Wdc3q43PEW6ZRmCGXaKSSVKwQIG+mbLZ99UPM10hd4KLjB6fn+HH1OLrxc2bKRQCbWK+szGTdz7DCWopGq8Unzu3CjXnZ1MZhtGzqmgKa4/FjxbXTtKKZOWdcoF9vc3qOH7+5KEv3c0yTi42TVrhCSolYpY6kkhvUIsagRE9uEfF3V9E1CqjevMRbptLnTUuiN0iz5NLx/FP/G70xWsF1YqZsCg8eV1S6lC5w0dadEy1BqmMze86w6DBDw21ptlyk0PO55k8ehLREOpFpVpSNEkoWZpqxkmbJxc+o3mYwJODdvd/pfo5ZcqFZds9ouEJKiVjWMAH667zJTWytryNFy+YlJnRFW0r9qQl47rpxWHbVSGoXObPkIpKW5JHcGK1Qi9ZM2TS2BbGzVLocEUmt2kjrTlI83XYpZs8ZgP6FZUJ2hu6yR1aQSyR63bE0raRWkw0pUvOMphfLSLlkJHuw/JoxWD1vIrbdPxlTh/tkw320srushkqMv1nzpepEq20z7XlSEyWMsIaRdqah2cEmLdGj+UCYnuPH1OE+2ezPcPu6Yt0lR8yQSyTeHm7Jot6sE3JIMFs2d6zai2VXj+yirJNc4CITo64cfRbVLHKz5QLQv7DEuV2YPeEcLN/4leZnWEEukdBwx9IIZQCsJxtSoueZmHRbkOPDyxRCyYyUy8OzcjrKX8m1ptZbwYLWWW7WfFm67hBe2lbGpLU1a7iFlBJGWMPWBMKTV24Ci79bQzGBe5ivl64DIc7tQu7A3pg15qyOjGvxRpuaGI/FBcN0j9EMuURS2dAq6RazQi1as2VT1xyQTK4g3fTF1/WQqM+oB7Pl0js5nloiRiR1TfrcdWbLJZKb87KoHKi09mYryYYEqbj2yJhtGsooYKxclq47hMLiCtlwn4r6Ftz2+l6s36/98prZk05SkJnzRWuzFrPhCikljOjMdC9WIXSqqkv0JBf/HQrRrW92qPIENfO/VALL3e98rvu5ZsglGjkFq6ODjIFJXpFYQTZA1+QKUiVBfB3tuCiz5bJ0Vg71jirr93+PlTuO6XqG2XKJJF9jK1EgunJDFZXxWEk2sZCKa5dT4vRipFxERWvhmgOKXqf5q/dh/X6Nyhgli6WZ84WkWYsV4QopJYyyhg0MnJ7k0YRCoF7XTG2WvVwJH7nNkJa7wmi5RKOkYE3P8WPb/ZOxet5EPHXtmI5OMnLz5Oa8LCzIH4w+vejc1M2WjVRyRawLXLR1p7yqifq4zJLLlKFnUG8BWVhcgdtX7aOynsyeL0A4VlCrBTn64vuXzUeojcsKsonEn5qAWydld4kH9UUl/LAuQWeUXMRcjLomZbNiSABuV1leTTy7PjxIz6po5nzRWzbNDHgMKUVYdmaKZGBglaEdMUjdq3IxPYtnDMPSdYeYx1EaLRcRksQLMXRB5Fxfr5jxTz/pn4G5L9Mp7G2WbCKJnEfiBe63r++FC12NEgJOW3eCIQGrd3/NZExmyOU3F9GpFyoiKhs0MXu+XDnmLE0WZBbdh6IxUzYZyfG4ITcLWZlJnWLy75s+TLFTkxEl6MyeM1KQNlaQOrtoYbZcrFDthRSukFJGTORZsfkwlm88zOx97sUq3GtQrBKJe1XuIKisb8Htq/axGZgERspFREviRayEr2BIwI6jdFyNImbIJpLoeSRe4BauOdDF4hFZFmx3WY3meowkGCkXFrGjrJQNM+eLFne9kY0ozJLN4hnDcOW4s7v8PPrCG41RSonZe0w0JB3jjLjEmCkXK1R7IYUrpIx49ZNys4dABRLrH0kJH6eiJ/FC7hBheVs3AxfCLkS5eVQv4X6rbwp09DJvbafTDtMKXDVOm+VPCZbKutHEmitKWKERBWu0dsErr2qkPBL7oKSMW6WbHgv0rCWz4DGkDNh5tDpmjItdILH+dYeDQA49iRdSsEo8MAu5pgEAeS1SO7fCi+alj8uoZ75uP/wj1eeZhdJcIcFOrkktaK3JyjLkxQ4oWQidenbpXUtmwRVSBuyQKQZuN9KTPJL1NaNx+kEghVRJFb048bYenVwRCWkt0k/LnbGeRGhmvgZDAjYcit2i1Q6kyjSYIMVOrkm16GmRGQ55sW/3HiWUpEGyRzv17JJr1mJ1uELKBGeoFLVNZBn2Tj4IpGB1+3TSbT05Pg5/v2kCtt0/WXZTJD0MVn6ir5SRlaCd+bq7rAb1zc7wxkiFbqjBiNJ7ZqC3RaZTlS4AKBgpbTAh3aOdenbJNWuxOlwhZUDugEyzh0ANks3MqQeBHEpWPz046eBobAvivnf3Y0NJpexrSA+DOocoXJHQ+q6dNGcAfdZjKzSioMmvcvt3tMjUs9c4VekCgD3HavHMnK4tqkn3aKeeXXLNWqwOV0gZMHFgbyR76fbeNguSzcxpB4Ec8y8ZROWAkMNpB0esbiEktUgjs+2dBI3vOhgSUHXCOa5Y0Xq8cnuZZqVUrhGFL8WLBfmDcXNelv6BGsRlOX7kDuyt2wtDs2+91ahsaEV6cnynOs9q9mgnn112vKxyhZQBcW4Xnrh6lNnD0IXaGEm5g6B3cjyD0RmPPzUBC6YOoXJAyOG0gyNWtxClw0D8940XZDMbnxnQij0Wi78vXXeIzsAsxNJ1h3DhY5s1J39FN6JYPW8iti+cgjvzh2Dx5SPw3HXjLH/R8aV4qcWnx7ldmDnaXrGEavjhREuXFtVq9mizu+mxwo4GDq6QMqJgVF/cOsmeh6nWGEmpg2DHoimOcIlce945zLMVRQXN7rKKJFbMpKxF65TLbf7kQchItrbyQAqt2GOnVWKQQm8vbiUFZXqOH0tn5dAaKhNmT6C33wRDAt7/3F49zdVAQ/ESz643fnM+0hLtvd+wSLg1Cl6HlCGLCoZj9NnpeGBtMWoa28weDjG+qI5BapCqranUkccuZGUmGfI+RnX7Mhol91GsJgEPz8oxtLkCKzKS4/HIlTm6wj2cWIlBCgHhg5W0044aCosr8F+rrT2fsjKTqT3LScmSkdCusxnndiFvUCaWXT0St72+l8ozjcau5Z5EuELKmIJRfkzLOX3YVp1otYybLSPZg8U/G4HMpDjUfLELj189CmemJndpO6cXOSXLn5qAmaP9eP/zCstvmFUnWrG26DvJtny0ERW0nUercfvre1Df0s7svYwilhVDqdNMwai+uPXbOjy/tYzF0AzjgRnDuiijwZDQRRFXwqnKhRSR1nWlTjtqYNFmlQU03a12jCWMBUvFa3qOHwvyBzPttMgKPcYkK8AVUgOIPGyDIQEvbStDZX2L6VaOmsYAfCkJ+Mk5KVj/BVAw0g+Ph427QskKds+lQzH+4Q04YVHFy+1Cp0tEdM95Foi39cd+Pop5Wzu99PT2QGNru+QYaVkxRG/Dfe/ux8lWa86TWER32ZHqyOVPTcAfZpwr+wy1ykVakge5A3rjw2L5agdWh6ZCZXWFnkV3HTvGEsaCteJF00LNmvmXDMLgPj0NMZawhiukBiPGCUq5sMVpNHnoGdj0hTHdV8KbfYoh7yVnBdtzrNayyigAROfjiPFtRhQeVur3bjZpiR4su3okACjOZ1pWDNHbsGLzEby6vcw25aCklAy5/tmV9S1Y8FYRHpsg/SxS5WL+JQORN+gMTMjOwO6yGksopG5X57XUKyEOJ1qCMf+uu1gLWVn9xGRJOSOIWM2itilgybAqF4A+KV78zy/HoOpkqyGKlxWU+Pg4F9qCsb+NvEGZ1DwIZsOTmkwgViLHby4aaNhYrLDwrHpIyO13sbLHaTM9x489D0zFgvzBXQLu003MFn56blghjzWfaSrtcW4X7swfjD2Lp2L1vIn4VW5/as9mgZSSQdIyVXxdNCSlssIVIc7tSOYxu3pDWqIHb9x8Pr5YelmnhMc9D1xK9Fm6i7WQVX1jkmoWj141Es9ZMNNcHN+SmSOQNyhTUxa9FqxQn3Tu+ecoVoOwc/KSHNxCahJKLuxgSIA/NYGpaynSahMKmmudNOOQiLYEpCV6cMMF/TEhuzeqTrbGjPVlEd+mRFgRG4L5kwd3mjOVDWGLmpGIc2figNOfO1ZiEm0ire2v7bBuJycp1yJJy1Qg7DnIG9Kn0+9IPCzRFrbIvzHF+uUCTrQGEN/DTZzwaJa1MBqWFsMV145B714JhqwXuTj+6PkZuYYzk734tLwGKz8pN80bYVZMZOSaMYtLR/hx/oDekuvW7slLcnCF1ETkXNhxbhcWzxiO21exWQzRkzkU22vGFLWHBA1eu2kCesS5ZQ+DtUXfET3HaOtu9JzZUWpsn3eljVApMYkVWuaOPzUBC6cPxe//eQAnW9lN/sUzhuHXedld5EQ6Z6pOShe9J1UupP5myfsHDe9rXt8UkA1x0fJZ9KCk0EshjmPf17XUk+p690owdL2QXBqj13De4Ez815TBuOftIvyz6HvmYxS/k5vysjB1uM/UmEhxbj667iCARkPfW7R8xrldhq4Ps+EKqUVJp1BQ/ooxfdHaHsLOo9WojYg/tNpkVntI6EG07l0wKJNKj2OzXYATsjOQ5IlDU8CYW4Vd505GsgdXjjkL+RGHXHl1E5Zv/Ir6mMQ5JqWMAuRzJrOnV/Z3WizS4t+s2HyE+udOT/JAEATUNXf1tsQq4WS0dV1OCU5L8uDXuVk4LysDVY2tnaoesMjMNyNUSculMc7twi/G91OtkLpdwMVDzkDRN3Wdzh8lrLa/TM/x46eDe+Ojwg+x7KpRqGlqR1qiBw+vP0T8mdTiQucLv9Hrw0y4QmpR9GxW4oH4P78c0xECYPXJLHdIpGsIth/TLxVF39R3+bkaNwdJIgDtbFgtxLldKBjpxzt7v4352sxkD6oa1W2id04ZhIkDMm05d6SU0EhY1pZVmmMkcwsAxvdPV3wPrcrFnfmDca6vp+5EuSFnJmPqCB8uGJgJCMDcl3fJvjZWiIvR1nU1h/yO0mri8CmXCxAINyqzL7NqmDiwN9KSPKrmy4rZY1Ewqi/W769Q9PbdNWUwss9Ituz+Io7nZ6NOV6FJTuihu1ZpDzcQDHU+19wuYN5F2V0UcjO8T2bAFVKLonWzklK67DKZ5Q6JDSWVqgrFXzgoE7ddPFCXm0NLrJ5Z/OmqkXh3n7JC6nYBH98/BRP/tFFVXdMBZ/S09dyhYQVXgy/FiyUzRyjOMZK5Jb6OFZG1bt/57Bu8p8Ede+kIH/572lAA5CEuG0sqLTOfSPdFUuPAFWP64tGrRuGSP2+JGRZht2SUOLcLy64iKxjvS/Fi9oRzEAgJ2H6kCn/84KDsa10A3vrsG2y7f7Il9lJSpuf48ZyO6icJHjdaAqEuPw8JwAtbyzD2nHTLWImNhCukFmVCdgYykj2oUWnRotENxkykDgnx8Hxl21E8sv6LmM/IHZCJvMGZikljJIqL0fFtWonv4cYtF2Vj5fajsq+Zd1E2EuPj8NjPR6m62dvJiqP24qU1dlnOWr8gfwjmTx5EdLAqza0/zDgXbWV7VIxIG2Kt26qTrZoU0twBmR3/n3SevFf0HX43wxoXOVJIP9s1552DxPg4LJk5IuYas8plVg2iEhYdh9ynVzzmnN8fWZnJKK9qwurdXxMXlTc6OZQmHZe60mq8vqscWw9XoZEgJt0FIMETJ6mQirDoUGYHuEJqUeLcLlw55iy8vL1c1d9JdYNxAnFuF264IBtPbTqiWBg9LcmDiac2NikFRa4YuVIyiB3idxYVDIdbCALtnZVS0QW0qCBc9kU8VB5cexDHT8hbcawSksASLZm0C/IH481PvyGeP0rIza1QsB3rDWxKpeXSEbnOAPECHR+zRXJNY8B2yofa8J2ONfbP/QA6KyjpSR48etVI2+7RSvthYXEFntz4laYcAKuW/otFnNuFvMFhA0i0oaO2MVypJXqvuPa8cxRjuO2spOuFK6QWJn+4T7VCGt0NRsQOcaRKiIpkrC49y64aKfu5lIqRKxW6t0vIw92XDsX69Udx/7RzUV7biv4ZSbg+NwvxPTqXGz6d3HJY0pJhtZAElpBmn4tKx/zJg7uU3tKzlqTmltFVL7RYiqPXWZzbhSvG9MUrBPuV3ZQPLeE7kckwt16UjZCrB3IH9sbEAexraLJGas4q1dYlwU6eGDmk5DItx99lr/hgP5k3wm7rhAZcIbUwag4KJYuWWqug1ZBTJCOJFbsXqxi5Uhaw3bg+NytmC1ixrum5vl6WD0lgTazscymlww4XFFLUVLlQWmdTh/uIFFI7Kh9awnfEufJfU4Ywa8lsFbS2ZHW6J0ZKSbVLBRcz4AqphSE9KJQsWiRWwSnnZsKqkNy8M5I92Hrf5C6WwEhIipF3RzeJXUISWBOZfd4dFXQ5hUt0MWZlJsWcG+IFWm6d2V354GtFHi3WPKd7YuS8knap4GIGXCG1OHIHRSRyByapVfCngy+iPm5akNy8axoD2HOsVlGRJN0wu6ObxC4hCUbQnZUOvZ89OibXypUptMLXijRarHlOvujF8krapYKL0XCF1AZEHxSZPb2AgE7Fm6UmL6lVcM+xWoaj1wctRZK7STikdGelQ+9nt0tlCj3YPR6fBaRWvz//fHTMc8vukOYqOH2daIErpDZBy0Ght0WhFaClSHI3CYdjDE62Mts9Hp8VpIlfeYOtGx5GAzW5Ck5eJ1qRD7rj2B4aLQrNRlQk5ZaoC2RFpsUNU/yb6GcA3ddNwuHQRrxAzxpzFnIH2j+zHDht+Yr2OomWr8LiCpNGZg1Eq58vtfO540tNkK1g4jTU5CoAzlwneuAWUgdDahUc3z8dHx0yenRk0OyYxN0kHA53OWuhO1Xp0EN3t/rxXAV9cIXUwdip/aUSNBXJ7r5hcro33OWsDV6lg5zuHIPNcxX0wRVSh0OizAUC6nvxGg1NRbI7b5jcOtZ90doYgkNu0dp+5Ee+proxanMV+H7cGa6QdgOcYhXszookDbh1LEx3PAS4y1kfpBatFVtK8e7e7/Dg5cMtXd/Z6th1jarxSvL9uCtcIbUxahYtV+a6N9w6FqawuAJL3i9BZUOEtyAlAUtmOvsQ4C5nfajpmieuqWfmjDZkbE7D7ooaiVeS78fScIXUpth90XKMg1vHwhQWV+C2U0XbI6lsaMFtr+/Fcw4+BGglW9jVcqUXNe1VxTW17MMvcPdQY8bnFJyiqCl5Jfl+LA9XSG2IUxYtxxh2Hq3u1taxYEjAztJq3P3254qvW7TmgGMPARrJFuv3V+CBtcWoaWzr+Jk/NQGLZwxDerLXtkoqqZJN0jVPRAA6WeE5YZRk7TRFTc4ryb0V8nCF1GY4bdFy2FJYXIGF7x4geq0TS5FIeRLkqG0KYOfRauQNcl7sn97GEI+uL8HzW8u6/LyivgW3r9rX6Wd28tQoeZqkLFyi5Wv5hq+wYssRE0duP2J59bqLosZLQ8nDFVKb0V0WLUc/cpZ0OZxWikTt5weAHaXOVEij+8xLIVcCbv3+7yWVUTns4qlR8jTd9vpepCV5UNd0ugJJpPKUNyiTWCENhgR4KI7bTLSGbJB49VrbQ0RjsLuixktDycMVUgtBstj13K66a/yXGpwiIyVLuhRuF1DbaN0WsmpR+/lPo/4v7ML0HD9umZSNFz8uQyjiY7pdwLyLsiWVx2BIwANri1W9jx08NbE8TQA6KaNAZ+Vp6nAfMpLjO4UvyLHnWC3yhvTRP2iTUZO3ELmPZvb0Ysn7B2N69f78C7IkMLsraiQJciTdB50IV0gtAuliJ12MX1Y24OWPW5HR04sze3mx62gVXt5ejsbWYJfn26k8CUuFUeo7SEv04Ma8bMyfPMiSB6scsSzp0YQE4PZV+7Dgh0bbfVYp1H5+kdwB9lkLaiksrsALW8u6HIKCALywtQxjz0nvoljsLqtBTaP6OsVW99RomR/RivYVY/rile3lMf+u6qT9L3pq8hbUhMkAp+cKBOgKK7ELJAlyJ1oC+Ki4AgWj+ho9PFPhCqkFIF3swZCA9mAIPb1xOBmhWErxzL+PxnzfCguWJ1FSOOWU9sUzhiM9OR6VDS2oOdmKtEQP6poDyOjphS/l9AbWcWNP9iIkCNhVVgNAQO6ATNQ3B3DHqq7fQV1zAMs3foVXPynDsqtGWtoFGcmGkkpNf7d841dY+UkZrhx7FqYO99nOQizOnw819BVPS/JgogWVJxpojT2vrG/W9b5Wda9qHVekoj11uI9IIc3s6dX0XlZBzdz5qLgSt6+SDwtRoqqx1fadBdUmyC1cc6CLJR4ATrYGcfuqfbj12zosKhiu673sBFdITYZ0sYdCAn73z2LJyasHAcCj6w/hnmFUH6t6sQRDAv6y6Su89HEZGtu6WnEBSCrt4aQK5Q0wKT4OLhc6WYcjWbGlFC6XsrO2rilgWmkgLbL8Z9H3mt+vtimAV7aX45Xt5bZPUFHDsqtG2n5Dl0NL7HlhcQWWrjuk632t6l7VO66NJZW4/7JhcLvQKfxBijH90nS9l9mQzp2/bDqMv2w+rPl9zuyVgNyBvam1iTaSYEjAis1H8Or2MtQ1S8cdRzN1uA9L3j+o+Nznt5Zh9NnpKBjV+e+dWvaRK6QmQ7rYozNZaXL8BF2XktrFUlhcgbvf/hxNbV0VxoqIBAOt0X1Sz41GIHy40XFxarKAx57dCwDw6IeHiGLbSBDlvyB/MOZPHmxZhU1LApOIL8WLJTNHYOpwH3aUVjvK4iCiNvZcjzwB67tX1RS6l+Ll7eVISYyPqYwCwPP/KUXu4D62nU+kc+epTdqV0ciYSbt1FiwsrpC1dIoJcjflZWHqcB/G90/HnmO1+OFEC6pOtKKyIfbZu3htMabl+Dp5Cp1a9pGZQvrII49g3bp1KCoqQnx8POrq6mL+jSAIePDBB/Hiiy+irq4OeXl5ePbZZzF48GBWwzSFSIvX4eMnzR5OBxsPHcdlo87W9Qy1i0WuWHk0tC3DWjEyLk5tFnBGghsPjgXe/PQbnHZy0WH5xsN4ZXs5brJgPK3WBKab87KQfyosYUNJJfKWbep0QPTpFY855/dHVmay5Q/FWKjJ7A2GBCxcc0CXMgpY270qxvGR7D1yvPoJWeWB5z8+ir/8u+xUPHqWpS92Uhhh5Z45Onwm2O1CGOv8EteQ6HHSQnVjW8eZ4/Syj8wU0ra2NvziF79Abm4uXn75ZaK/efzxx/GXv/wFf/vb35CdnY3Fixdj2rRpKCkpQUKCNV0/atHrVmTJsg+/wKU5Z2meyCSLZcn7B9ErwYOqk63I7OnFgyozeK2AEXFxWrKAGwOxLcF6qD8VT/vix0fxy5+cbZkYU7UJKi4XcMm5Z3RSRqUOleMn2rB842mrT3qSB7/KzcKAM+ynoJLUIc1IjkdlfTPuf2e/rgugL8KCb2UFY+pwX5dLnRrU/l04Hv0wXv2k3Fbx6OLcYXlmvf3Zt1hb9H2nC6HVXdDiHm0EG0oqkTuwt+PLPjJTSB966CEAwMqVK4leLwgCnnzySTzwwAOYNWsWAOC1115Dnz598M9//hPXXnstq6Eahl43GGsqG/RNZJLFUtnQirkv7dI4QmtghMVAa5a4EZxsbbdUjOlGlQlcggBs/uJHbP7iR/T0xqE9SFb/sLYp0Mkt6U9NwO8vG4rjJ1pxrKYJ/TOScH1uFuJ7uFWNhxXRsce/KxiG/1otHfojIGyJWRCjm1Usrh53Fh7/+WhsKKnEhY9ttnSM2+6yGlM8L3VNAVnXqhUTVeLcLiyeMYxp2FitxPdQYXEXtJF79Dt7vsXIs1JR+mMj0eutmkwYC8vEkJaVlaGyshL5+fkdP0tNTcX555+PHTt2yCqkra2taG09fatqaGgAAAQCAQQC5rl5gyEBe47VdlgCx/RLw6PrDiI+znrqqNctdPzvS1sPIxRsx/j+6ao3wh/qG+G14OejiS8lHKvJem5pkWXk92gUNSebcdfqPVh+zRjkDzO+1uLGQ8fx+s4yeOO0/X2gvR0ANP19zclm3POPzof044UlOK9/Gsb2S4fLBZyX1RvnqVQqxLmlZ45tPHQcyz78olP7SrdL2+dUwweff4tvqxtQ9G14H458v1oT50r0fjy+fzrz/SrWenx03UH8dHDvjrkh9Z35UhKw8LKhpqytSFIT4kzb2//0QXEnORmN3HqsrD1pmExaAwEsfLcIANkazkzqYar+E4macbgEgTSdQxsrV67EXXfdFTOG9JNPPkFeXh6+//57+P2nb0O//OUv4XK58NZbb0n+3ZIlSzqssZGsWrUKSUlJusbO4XA4HA6Hw9FGU1MT5syZg/r6eqSkpCi+VpWFdOHChXjssccUX3Po0CEMHTpUzWN1sWjRItx9990d/25oaEC/fv1w6aWXxvzwLNh46DgWvFVkWbe8FF63gKU/CWHxZ260hk7fQl2AKmtGMCRg2pNbcbxBW+aqHegZ3wNb77tEs1tWygqSnhSPn43045KhZ3ZYpoMhAZcu34rjKlwvct+jUbxyw3mGZlU/++9SPP1v+/QTf5JwLQUCAWzYsAFTp06FxxO76aTUnLI6Rs0Vuf1YrHPZM74HTra1M3lvkvWY5InDw1fk4PGPvpT9/lwA+qQk4KO7JhlqJYy0Kh9vaMH/bPjKsPeWgnT90EZcj57+Y/DYR4ctv85uuqA/7r7UOB0sFqLXmgRVCuk999yDX//614qvGTBggJpHduDz+QAAx48f72QhPX78OMaMGSP7d16vF15v18LDHo+HaDOnSTAk4I/rvkRL0DqB+2poDbnQGjX2P677kjjRyQNg0YwRHf2ynaiUtjYHceET/8GjGpISCosrcPuqz0/JJaL4+IkAXvrka7z0yded4uyu/kl/LN+o/hCQ+h6NoKqp3bA1FwwJeOWTr035nFp56AN1SYMke5jcnLI6RswVkv24tTkI1nJTWo+twRDueHP/qX/Jj+NYbSv2fXvCsEQV6eRbc+eXmrOIBXf/48CpuWTtdfbsx18jwZtgmWooata5KjPPGWecgaFDhyr+Fx8fr3rAAJCdnQ2fz4dNmzZ1/KyhoQG7du1Cbm6upmcajZUTUbRSUd+CFSqKHYsdKHypzqiKIEXtqaSEQhXdgEhLE4l1PwuLK5CVaa+Qk/KqJsPea3dZTacC1HagsqEVu8tqqD1Pa7krK9DdEwO1YFSiiph8azXZidnjRhM8VWzWTuts+cavkLdsk6ozygowSwf9+uuvUVRUhK+//hrBYBBFRUUoKirCyZOn624OHToU7733HgDA5XLhrrvuwsMPP4z3338fBw4cwK9+9Sv07dsXV1xxBathUsWumW2xWL7xsKqJPT3Hj233T8bqeRPx1LVj8MbN58OXkmDxe6V6HvpXScdmFQu1h+PCNQds13LwzU+/JpaHXvS2tDQLmnuEXRWuXglxqKhrxo7SaqbzxWn7sRFKvNUvOWZ8p3uO1Rr+njSobGhVbTgxG2ZZ9n/4wx/wt7/9rePfY8eOBQBs2bIFP/3pTwEAX375Jerr6ztec99996GxsRG33HIL6urqcOGFF6KwsNA2NUit2iaPBmqL7ca5XZ3cS0tmSvcotitq672p3UjrmgL4tKwGvhQvUTcPK2Bk/buqk3Q6URkNzT3CrgrXiZYg7v5HuLwUy1JQTtmPjep6FQwJWLm9zNKXHCO/U7EE1yvbjmKm/Up6dmCnQvnMFNKVK1fGrEEaneDvcrnwxz/+EX/84x9ZDYspJMWnU3UUYjYTvcqG6Mq3alMArZAqBVo20mf/Uwob7CGd+PDUbZx1/cS6JvsppMnx4ZqnwZBARTaZyfayoEvBstakEQXdWcOy61VkzdOjP57EazuOSdYDtQrJ8XEICQK19aNEZAytN06wrUJqt0L5lqlD6gTEdnRSlkBx+Vzzk7Px5qffoL6ZTWYnS/RaZMQexSu3l2HpukOURmUupIqmlt7Zre1kBdutxGs7juG1HceYF0F32UxRB4DGtiCuf2U30pI8dDr12FAGctCy4kQXlv/ZKB9e/LicziBNwMdgHQVDAlZsPoxXt5fbKg67sS2IuS/tQkZyPB6elYOCUWz2Fqs3sNGCXbwpXCGljJwl0JeagJmj/Xhha5ltJzoNd0mc24XMXva37ABhdyOpG03psuJEKhl3WckdkIkVW0qpP9cI6poCuO31vXhOp2yqTtojlCMWtKw4Vm7LrIb5lwzC4D49mXRqKiyuwMI1B2zppROpaWzD7av24tZvs7GoYDjVZwdDApa8f9Bx+7Ndwle4QsoA0RIYeVMf3z8dFz+xxbYT3e0CxvdPp/IsuyyOWKh1ozk1bEEKAWEDHqv4pYkDe+vqQ24FFq05QCwbqZaSTllHInqsOE6yauUNymTiXnWSjADg+a1lGH12GgpG9aX2zBWbj9gmZp8Eo+KPacEVUkZEJ/XsKK22tRISEsLZhjQ2ytpG+8X/RXNzXpYm65Z4WXll21E8sv4LBiOzDqzjl268IAvLN5KXJLMatU0B7DxajbxBmYqvk7L8+VMTsHjGMKQlemzldlVCq4Jt9cxwNajxuqjBSTKK5Pf/PIBpOX4qF97C4gpNdZ+tCsv4Y1YwK/vE6cy3NY1mD0E3NOJQgiEBS9eVUBiNueQP92n+2zi3CzddOABpScY2bjCLD4srqJb4KSyuwIWPbba1Miqyo7Ra8fdyNSEr61twx6p9uHCwsjJrJ6o17i92LX8lxc9G+bG7rAZri76jumacJKNIapvasWKz/m5toqveSfhSE5iFTLGCW0gN4v9Kjps9BN1UnWjVneHohI2RhhUjzu3CsqtG4rZTXa2cDM1EJ6e5HZWiiZWsWmJIxJ5jtUhL7IE6GyZJRvOHfx3EZaP6qt5f7JKwQcJL28rw4sdlHf+mlRzoJBlFs3zjVzjX11OXjHaX1djeVf/7gmEY7k9BVWMrk/hjI+AKqUE0B+hmTD+BObjKE842FgRgTQC4F6uovkc0S9cdwkvbynRtkBtKKimPqjNGyIWWC2R6jh/PzBmH21cZo5SaMWci0ZLoFBk7mZnsxZL36bsdzZRL7gB5C+eeY7WKlzcxJGJB/mAm1mKj5VLTGNAU3mFGLC0r2URVQuzo3PbMnLG6YiWNkpFZa0lvrDprhZ2lXMQ40ZsuzLadAhoNd9kbRDbFNpClnjn4uReIiwPc7vD//twb/jlrRKVCS/eHwuIKvLK9nP6gTmGEXOZdpC12VI6CUX7ceEF/as+Tw8w5IyKetaQdrkTX/OwXd+LON4sw9+VdqGyge3CYKZe0xB6YqKB8kWbRZ2Um45k5Y2kNC4B5ctGiGIgl1Yw6is2QzfzV+7B+v7o9NxgSsKO0GmuLvkMoJCAtkW2IkJlrSW9bUZYKuxFysVOcqBJcITWI31EqT1HqmQO3zLfmdrNf/GqVChHR/cgKo+Ty4sfl1FuxpSWxLYNl9pyJJDLRSQkj+mmbLZdlV49SPERIW8ee2SsB03L8SPTQ2c7NlIsWxUAsqQawL81qlmxCAnD7KnJDgNRljmXym9lrCdBn5ZyQnYHURPoOY9Zy6eEGbrigP76rbcZ7++jGHZsBV0gZEQwJ2H64Cn/+6Av8+aMvsffrWozo20vXM5/A6ckdXRhc/LfbHX4dS0iVikhYxo4aLZeFaw5QW/TBkIDVu7+m8iwprDJnolE6PIzICDZTLqmJPYhqkI7vn65o+XPhdDzz7rIaKmFBZspFT2y2WFLNl8rO0mWFtURiCDDiMheJFeQCkF/g5HBR7rZhhFzaQ8DKT45h6bpDWPBWEWa/uBPjl27AUxu/sqViyhVSBhQWV2D8wxsw9+VdWLGlFCu2HMHcl3bhWHWzrueKMShy60b83VUGJW+ruZGyjNExWi51TQHsjJEdTUo4mN45siFFyRJmROKbmXK58YIsTCWo0qBk+Ysu6UJrfZkpF71ux+k5fmy7fzJWz5uI5deMQUYy3UFaYS1V1Ldg+YYvZS1hZpR3soJcAOjqNrK7rIZ6TWOz5FLXHMDyjYcx/uEN1L15rOEKKWUKiytw2+t7JSf3yVZ9mbCkFzij2iqqca+xjNExQy5v7Cqn8hzWwfRmyCYjOZ7IqieHERnBZq6lJzcdQd6yzUSHhZzlL7qkC631ZZZcenp7ECnpsRDrP/tSElDTSFfBsMr+u2JLKWa/uBMXPtZ1DplRxcQqcqlq1J4lz2LPMVsudU0BzfkeZsEVUoqwrmUWnYGp93V6UNu5SUw8YIEZcllffJzKQmed/WqGbGaNDitJsax6chiREWz2WqpsIE8OjLT8PXXtGKyeNxHb7p/cyeU/ITsDvhT9schmyeVka7uupJRIgiEB249UUXlWJGbPmWikEkzNKO9kFbno2TdY7DlWkIsA9fkeZsIVUoqwrmW2JhCevHITWPzdGgMat4idm0gR3Y8sLoNmyYXGQmepqAPmyOadvd/hNxdlx7TqyWFE1rRV1hLpHBItf7PGnIXcgb27KPRxbhdGnp2qezxmyoWGMiUm86zYor9YejRGyEbNnJdKMDWjBJYV1pK3h1tXbWgW+7AV5ALor0BgJFwhpQjr2+m9WIXQqbyF6Eku/jsUMq6GotrPK7ofaXcoMksuNBZ6ZJwgC8yQzYmWdrz4cRl+NsqnaNWTw4isaSusJS3JgXKs31+BDSU/6H6OmXLRq0yxTuYxQjaJ8XGqXh89h4wugQVYYy21tYd0GQdYGEysIBcRuzRG4AopRYy4nQ4MnJ7k0YRC4d8bhZbPOz3Hjz0PTMXlo/THi0VillxoLPTpOX48x0BRFzFLNi9+XI7axjZZq54SRmRNW2Ut6Z1DwZCA+97dT2k05shFb/czo5J5WMumqS2IO6cMVm2tE+eQkSWwIjF7LQkA/r6jXNczWBhMzJaLiBmWcy1whZQitOK4YjEwsArvtALBYHhSB4PAO63GKqN6D5DPjtXRG8wpzJALrYUuKuoL8ocwKWBt1pxZvLZYs+UiOnby0uF9KI/OGmtJ7xzaWVqtO2EyGqPlojfD3shkHtayOVbdiP/cewlWz5uI+ZcMIvqbyDkkd5lLS/JQq1Urhdlr6VhNU5efRTYHIKnReXofHowkjzprtRxmy6V3cryqfA8z4a1DKRLndmHJzBGG9Ce/F6twrwGxonI0B4LYUFKpqWsRy8PDSLmkJXl097SPJM7twp35g/Hbnw7ExEc3Us8SNmPOVDe2aWoHKSLGTgJA1YlW/F/JcZrDA2DeWhJb/umdQzuO0k/gAYyRS09vHP78i9G269XOUjb/LPoeu8pq8ODlw7Fg6hC8u/dbVNa3SFp/5ebQ9Bw/pg73dbTdLa9qwpMbv2JuQTbzXOqf0bkbYmFxBR76V0mns8afmhCz9XV4Hx6Cn/TPwNyXd1EZm5lyqW5sw8VPbNHV8tsouIWUMqL7NUllLJDdqNdRUsIu8SyxYOUS23OslroyaiaV9frq74pcn5sFB3THA0BecYAEe+TPSrP0ipFUDkm7uCRJETPoN5RUEteijUa8zP1sVF+8+enXtp4nsXC7wvuDiFw8sZrW13rKSFkNPS2/jYQrpAyYnuPHvIuyzR4GU7S2EAWcc3jUNgWoZy+yKlljJjWNbVSeE9/D7Zh1RVpxIBbr93+Pv31SRmlUxuNLobMXGBUuZRSR++vU4T6iWrRymFGb1GjmXZSN+B5hdUYpnljNueWUcwrQd14bCXfZMyAYEvDWp9+YPQzmRGZ4qnHJipmgcm4oO7GhpFKzOzoaKReTE8jQ2dIvkkUFYWvRix+XwcL7qiTzLxmEwX164sxeYRerXsvoo+tL8PxWeyqjtMIVROLcLsyecA6WbzxM5XlWIHJ/jXbBq5lDTvFITR1+JjYd+qHTune7wsqouC8AsRVw0nNrQnYG0hI9aG6jc6FmjQvK3hKt57WRcIWUAazrkVoNtRuemAn6WwNibVmztuh7/H6Gfrer6GKymY5FBC0rmMiiguG459Kh+N2a/Xhn73dUn82SvEGZ1A6C9fsrbKuMAuHDceZov+51E0lWZjK1Z1mJyAx6LfPH7pY+lwt4evY4FIzyo609hL/vKMexmib0z0jC9blZHZZREdLzKNbr4twu3JiXhWe2fKV57EZCenZY+YLCXfYMsPIXzgKt5Z9umWR/96uYtKMHM/pPG4XeagxyxLld2F5aTf25LCBpl6qGYEjAA2uLqTzLTF7YWkY1ps3uipccej+XGbVJafL07LEoGBUOS4jv4cbNFw3AH2fl4OaLBnRRRgFyeZG8bv5ketn2VsHK64QrpAyw8heuFqVNTM9BGwwJeP9zawdYk6L3AuLEGC/Xqf9oJO1IsbO02hYyo5m8JLK7rIZaXK7Z0Ixps7viJUetju86GBKwu6wGl+X4bHfhTUvy4LnrxmFajl9V6aZY80DNuRXndjEpN8eKjGQPlc9tFlwhZYAYe2J1vBK3y2jklr7eg9ZJSpjeC4gTLeq0knakKCyuwB2r7BHuwUIOTpkvNDtVAeYVhWfN0nXalHaxjersF3file3lAOwhl7QkDxbkD8GeB6YCQMdnuPPNIsx+cScufGyzomVdaR5oObcmDsxU+xFMwZfixcOzcgDQ+dxmwBVSBoixJ1antV2mhYQE0XNY70HrhEOV1o3TSRb1+ZcMUtUmVC1irG1ds/XLYi2eMYyJHJw0XwC6e4ERHb6MRovSLlf2yOpW0vmXDMSeB6bizvzB2FBSqbp0k1gIv7U9hLvyh6BPVPy6lnPrzF72qN7Q0h6C2+3SVZHBbHhSEyPmTx6MVz8pR12T9Q9OEkJC+IDN7OWlkiXslEOVxo3TSVUHTrYGmGVw2inWNi3Rg1/nZTOxRojzxSkehsxkugd+ZEb6h8UVeG3HMarPp8mos1Ox/9v6mK9To7TbaZ1EkzfoDMS5XTFLN7lwuiSWuMakqpT4UrxYkD8YWZnJms+t8f3T8dEh7Z/JKMTa4M9eNw7b7p+sqSKD2XALKSPi3C4su2qkLVwkpGT28mrqSy6FE+K9bpmUTeXGSeJisgtr9n7HrM6dncI8bszLYnYAiPNF6elTh5/J5L2ZwEBMYkb6ZRa3CJEoo4C6C7yd1kk0YrysmtJNgLxF+HhDK57ceBjeHm7N55YdFDmgc61RAMgd2JvaeW0UXCFliOg+8jvEfUTTqikeqna8xYu8/3kFNeVLztXoS03AgvzBVN7DCBpa2qk3CxCxS5hHepIH8yez/c7k9pbeyfH46+yxKP6ugen706TqJLsSeXa4+Lpd8jq5lrAg0nVixTwHMV5WTekmWoXwY2FFeUVDOy7baLjLnjF2ch/JQbuItcj0HD9uysvqCLi3G7SLDMsVvwaANz/9xlSX/nXnn4PXd31N9FpWiqNdwjwevWqkIRYJufliNwsZy+81suZxrMLhZiHqSdHj05qIQirPp+eMg9vtOtXrvtESTQXEPVVN6SZahfBjEY5bt/LV5jR2ubxHwy2kBmAX95EUrLPzpg73UX8mDUhLfdBe+OJciXS1RLr0zSAj2YM/XD4CGcnxRK9npWCQlHMx0zOV7I3DcwYnDkjNF7scRkaVoZHzPmQkezBl6BlIjjf/GLwpL4taIgpp2aOJA3t3zJ0784fg1knZpq4fkR9OtKgq3USrEL6TsMvlPRpuITUQOyav+FIT8ODlw5kdslaTiWgNvuGCLPxfyfGYrzdq4YuNBMzoznP1uLMQ38ONh2fl4PYY5ZZYKhhK1i7x4Jp3UTZeOCUjo+dTL28PS1yw7HAYGV2GRqn15vYjVZj70i7mY1Bi6nAffj9jOJVEFJJ1Ei33wuIKvLC1zBJ78Jm9ElR9BpqF8K3OvIuy8cH+CtnzkpU30yjMvxp2I/RYuoy6uLoQjkNb/svRTMv3iFitdqCA8EY3cUBvasWVaWBmI4GXPi5HYXEFCkb5catCdy2WhfBFlGJtn71uHBYVDJf8fWoC+7t3ZUOrJWK37BA3aUYZGilrMsA2hpUEcR+RG58WYq2TSLlbKSs/ck8l/Qw0C+Er4Uthv6Zu/+kALMgf0qXdcu/keDwzZxx+P2M41RqrVoNbSA1GXGS/e6+YuNuKPzUBm+/5KV7feQyfltcgKT4O/TIS8Y/PvkVlA73NVJzCj1yZY+hBIcokumSHGdw1ZVDHZ1drZWCJ2XGBYomVRQXDMfrsNDywthg1jadLmvkZW9IjUbJ2yf0+JAiGWMGs4BaMtC5ZifmXDMLgPj0tV4amvKqR6vMykj2YNbovPjzwHYCg4mtZXuJirRMRs/eWSKJlQfIZtFiEtbDwsqG4fdXnup6hRFqSB/dcOhRxbhfmTx6kuL9JnZesvZlGwBVSE5ie48fkoX0w8dGNnQ51KcQNKzE+DvMmDcC8SQM6fndX/rnYXVaDDSWV+GfR97rbCfZJScCiGSNMmdCRG8/Gkkq8V/RdTNmwILJXgJUWvpmKTnRSQMGovpiW4ze1zp1oTSL9/dqi73S9X6InDs0BZeUCsI5b8PTF94Ap60iKvEGZzGrUaiUYErB6N1myXiwuHd4H52dn4PrcLMT3cGNCVhrayvbIWtXSkzx49KqRzD1QsWRuhUuU2wWsmB22eortTiP3llifwYi9On9YHzx73Tjc/+4B1DNozLEsIhky1vdGetmwG1whNYn4Hm786cqRHVYMKXdJrA1LnLS5A3t3iT+qbWzF0nWHiG6+4XIWQXx01yQkeMkSV1gQ+Xl+d+rzbD/yI1ZsKTVwFJ2/CassfCsoOpEHF8lBZyX0yM8FICUhDgkeN2plGl1YMXbr9MV3E/FlNS0pXNqGZkMPK8pGZHdZDTUv0/+VHMf/lRzHS9vK8ODlw5E/rA/Wl4Uv+sdqT79HWqIHN+ZlYf7kwZZQIKywt9x8YTYKRvkli9uTel+M2Kun5/jxTU0THln/BbVn9ukVj4dmqfdK2m0PJoErpCYid6vTsmFJTU7RirWhpBKvbC+XdWcsmTkCbWV7LLE5ioifh8btvVdCHP7yy7G48bXPYr42d0DXvsVWWPixkr9cADKS4gE0MxuDFQ4urehJnhMAHD/RhgX5Q/Dkxq86fiZi5dit8MU3R/LiK470rvwhyMpM6lRmrPPltg1L18mH00TuK1YIb1EDC+ug2NrymTmjAQAf3TUJ+749YVlLFsnekprkQX1TgFmc6Qf7KzC2XzruWLW3y3uI8iSJOTZir87sSbez2P9eMxZ5g7qeO90RrpCaDMtbXaTFcUJ2hqw7Y8q5mVhvfPI2EWqUILnD8Imfj8akoWciLcmjaPlJS/JgokVvnCRxUjNG+gHhKPX3trKFixQa9SizMpMsE8KhBi3uzK6XW/lwGvE5AGwnGzX7S3qSBwJiW4/F1pbLPvwCdw+1xoVWCZK9ZdlVIwF0/X5F/KkJmDnar7nCRUV9Cx5YW6yqVahZ+FITqT7P7KQ6K8EVUgtgxIalpPgGAtaIMZOC5PbuS03A4hnDu1hxog/DZVeNxG0KiR7LDCporpVYikWveDeqDmlTSF0uQBDsZ+FSg5z8enrjcLI1dnxoZrIXeYMzLRHCoRa9F1+pcBqp59hNNiSW87QkD56ePa7jskoSSiQAqGwwPzaTFNJLi/j9Vja0oOZkKzKS4+FLTez4nseeky7p8asjiLlUCiuhVdyeBuKcoZUIZmfPE224QtqNsPpNXQrSDMrpOf4OK47cYTg9x4/nrhuHJe8f7BQ35kvxYslMc5K51KKkWLS0tuGjQ9rKZwkCsCB/CN789GtbWbjUIspvxebDeHV7OeqaA0TKKIAOwdpxHQH0xq30HLvJhtQ6mDf4tEuVViiR1SDNaFebbEOzwoUV5B45Z/SGMBhZOtAOcIWUY3lIb+8kh6FVkpT0IPc5Iz+DFrd0VmYStt0/2dayIWFDSSWe3HhYtXy4a82ZaAlpcKpVi8aFIvoZwZAQ08uVnuwhqgZhFbnTKFVoRN1mu8EVUo4toKlI2s2Ko5bl14zBH9d9qXqjFDukOFk2eoqAW+Uw5NBH7f5CFEqUkgCAbo1TO0JihX54Vg6Wrjtkqw5EkXNGqvSiWB0gFMKpus1df+cUzxMtuELqYKTqudn5NuZ0ZYkW+cP64NKcszq++8yeXtzzdhGON7TaZrNnhZYi4N1JPt0ZNfsLiZK18LKhaCvbQ3uYtoTECu12uyzTiIQUpdKLkedtrHAyThiukDoUPfXcOPYn+nBdMnOE7TZ7FqiNQetu8uGQE0vJsnL1EjMg6bBmxyoWIk6KrTYLrpA6kMLiCsmAazX13DjOwu6bPS3Uut27m3w46rBr9RKz6K4diDhkcIXUYSjFyFmtnhvHWPhmT1bmJyPZg8U/GwFfSveTD0c93PpFFy7P7gtXSB1GrBg5K9Vz4xhPd9/sSWL//nQl2/7iHA6Hw+mK2+wBcOhCGiNnhXpuHI4ZiOELvtTO7ntfagIPZ+FwOByT4BZSh0EaI8dL2HC6Mzx8gcPhcKwFV0gdBmmrTV7ChtPd6e7hCxwOh2MluMveYYgxckDXFpK8hA2Hw+FwOBwrwhVSB8Jj5DgcDofD4dgJ7rJ3KDxGjsPhcDgcjl3gCqmD4TFyHA6Hw+Fw7AB32XM4HA6Hw+FwTIUrpBwOh8PhcDgcU+EKKYfD4XA4HA7HVLhCyuFwOBwOh8MxFa6QcjgcDofD4XBMhSukHA6Hw+FwOBxT4Qoph8PhcDgcDsdUuELK4XA4HA6HwzEVrpByOBwOh8PhcEyFK6QcDofD4XA4HFPhCimHw+FwOBwOx1S4QsrhcDgcDofDMRWukHI4HA6Hw+FwTIUrpBwOh8PhcDgcU+EKKYfD4XA4HA7HVLhCyuFwOBwOh8MxFa6QcjgcDofD4XBMhSukHA6Hw+FwOBxT4Qoph8PhcDgcDsdUuELK4XA4HA6HwzGVHmYPgDaCIAAAGhoaTB6JfQgEAmhqakJDQwM8Ho/Zw+FohH+PzoB/j86Af4/OgH+P+hB1MVE3U8JxCumJEycAAP369TN5JBwOh8PhcDicEydOIDU1VfE1LoFEbbURoVAI33//PXr16gWXy2X2cGxBQ0MD+vXrh2+++QYpKSlmD4ejEf49OgP+PToD/j06A/496kMQBJw4cQJ9+/aF260cJeo4C6nb7cbZZ59t9jBsSUpKCl9wDoB/j86Af4/OgH+PzoB/j9qJZRkV4UlNHA6Hw+FwOBxT4Qoph8PhcDgcDsdUuELKgdfrxYMPPgiv12v2UDg64N+jM+DfozPg36Mz4N+jcTguqYnD4XA4HA6HYy+4hZTD4XA4HA6HYypcIeVwOBwOh8PhmApXSDkcDofD4XA4psIVUg6Hw+FwOByOqXCFlMPhcDgcDodjKlwh7aY88sgjuOCCC5CUlIS0tDSivxEEAX/4wx/g9/uRmJiI/Px8HD58mO1AOYrU1NRg7ty5SElJQVpaGm6++WacPHlS8W9++tOfwuVydfrvtttuM2jEHAB4+umnkZWVhYSEBJx//vnYvXu34uv/8Y9/YOjQoUhISMDIkSOxfv16g0bKUULN97hy5cou6y4hIcHA0XKk2Lp1Ky6//HL07dsXLpcL//znP2P+zb///W+MGzcOXq8XgwYNwsqVK5mPszvAFdJuSltbG37xi1/gt7/9LfHfPP744/jLX/6C5557Drt27UJycjKmTZuGlpYWhiPlKDF37lwcPHgQGzZswAcffICtW7filltuifl38+bNQ0VFRcd/jz/+uAGj5QDAW2+9hbvvvhsPPvgg9u7di9GjR2PatGn44YcfJF//ySefYPbs2bj55puxb98+XHHFFbjiiitQXFxs8Mg5kaj9HoFw+8nIdXfs2DEDR8yRorGxEaNHj8bTTz9N9PqysjLMmDEDl1xyCYqKinDXXXfhN7/5DT766CPGI+0GCJxuzauvviqkpqbGfF0oFBJ8Pp/wxBNPdPysrq5O8Hq9wurVqxmOkCNHSUmJAED49NNPO3724YcfCi6XS/juu+9k/+7iiy8W7rzzTgNGyJFiwoQJwh133NHx72AwKPTt21d49NFHJV//y1/+UpgxY0ann51//vnCrbfeynScHGXUfo+key3HPAAI7733nuJr7rvvPmHEiBGdfnbNNdcI06ZNYziy7gG3kHKIKCsrQ2VlJfLz8zt+lpqaivPPPx87duwwcWTdlx07diAtLQ0/+clPOn6Wn58Pt9uNXbt2Kf7tG2+8gczMTOTk5GDRokVoampiPVwOwp6JPXv2dFpHbrcb+fn5sutox44dnV4PANOmTePrzkS0fI8AcPLkSfTv3x/9+vXDrFmzcPDgQSOGy6EIX4/s6GH2ADj2oLKyEgDQp0+fTj/v06dPx+84xlJZWYkzzzyz08969OiBjIwMxe9kzpw56N+/P/r27Yv9+/fj/vvvx5dffok1a9awHnK3p6qqCsFgUHIdffHFF5J/U1lZydedxdDyPZ577rl45ZVXMGrUKNTX1+PPf/4zLrjgAhw8eBBnn322EcPmUEBuPTY0NKC5uRmJiYkmjcz+cAupg1i4cGGXoPno/+Q2S451YP093nLLLZg2bRpGjhyJuXPn4rXXXsN7772H0tJSip+Cw+FEkpubi1/96lcYM2YMLr74YqxZswZnnHEGnn/+ebOHxuFYAm4hdRD33HMPfv3rXyu+ZsCAAZqe7fP5AADHjx+H3+/v+Pnx48cxZswYTc/kSEP6Pfp8vi4JFO3t7aipqen4vkg4//zzAQBHjhzBwIEDVY+XQ05mZibi4uJw/PjxTj8/fvy47Hfm8/lUvZ7DHi3fYzQejwdjx47FkSNHWAyRwwi59ZiSksKtozrhCqmDOOOMM3DGGWcweXZ2djZ8Ph82bdrUoYA2NDRg165dqjL1ObEh/R5zc3NRV1eHPXv2YPz48QCAzZs3IxQKdSiZJBQVFQFAp4sGhw3x8fEYP348Nm3ahCuuuAIAEAqFsGnTJsyfP1/yb3Jzc7Fp0ybcddddHT/bsGEDcnNzDRgxRwot32M0wWAQBw4cQEFBAcORcmiTm5vbpewaX4+UMDurimMOx44dE/bt2yc89NBDQs+ePYV9+/YJ+/btE06cONHxmnPPPVdYs2ZNx7+XLVsmpKWlCWvXrhX2798vzJo1S8jOzhaam5vN+AgcQRCmT58ujB07Vti1a5ewbds2YfDgwcLs2bM7fv/tt98K5557rrBr1y5BEAThyJEjwh//+Efhs88+E8rKyoS1a9cKAwYMECZNmmTWR+h2vPnmm4LX6xVWrlwplJSUCLfccouQlpYmVFZWCoIgCNdff72wcOHCjtdv375d6NGjh/DnP/9ZOHTokPDggw8KHo9HOHDggFkfgSOo/x4feugh4aOPPhJKS0uFPXv2CNdee62QkJAgHDx40KyPwBEE4cSJEx3nHwDhf//3f4V9+/YJx44dEwRBEBYuXChcf/31Ha8/evSokJSUJNx7773CoUOHhKefflqIi4sTCgsLzfoIjoErpN2UG264QQDQ5b8tW7Z0vAaA8Oqrr3b8OxQKCYsXLxb69OkjeL1eYcqUKcKXX35p/OA5HVRXVwuzZ88WevbsKaSkpAg33nhjp0tFWVlZp+/166+/FiZNmiRkZGQIXq9XGDRokHDvvfcK9fX1Jn2C7slf//pX4ZxzzhHi4+OFCRMmCDt37uz43cUXXyzccMMNnV7/9ttvC0OGDBHi4+OFESNGCOvWrTN4xBwp1HyPd911V8dr+/TpIxQUFAh79+41YdScSLZs2SJ5Forf3Q033CBcfPHFXf5mzJgxQnx8vDBgwIBO5yRHOy5BEARTTLMcDofD4XA4HA54lj2Hw+FwOBwOx2S4QsrhcDgcDofDMRWukHI4HA6Hw+FwTIUrpBwOh8PhcDgcU+EKKYfD4XA4HA7HVLhCyuFwOBwOh8MxFa6QcjgcDofD4XBMhSukHA6Hw+FwOBxT4Qoph8PhcDgcDsdUuELK4XA4HA6HwzEVrpByOBwOh8PhcEzl/wO5V2WawucnqwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.axes().set_aspect(1.0)\n",
    "plt.grid(True)\n",
    "plt.scatter(tf.math.real(y), tf.math.imag(y), label='Output')\n",
    "plt.scatter(tf.math.real(x), tf.math.imag(x), label='Input')\n",
    "plt.legend(fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dc0b529",
   "metadata": {},
   "source": [
    "Let's now *optimize* the constellation through *stochastic gradient descent* (SGD). As we will see, this is made very easy by Sionna.\n",
    "\n",
    "We need to define a *loss function* that we will aim to minimize.\n",
    "\n",
    "We can see the task of the receiver as jointly solving, for each received symbol, `NUM_BITS_PER_SYMBOL` binary classification problems in order to reconstruct the transmitted bits.\n",
    "Therefore, a natural choice for the loss function is the *binary cross-entropy* (BCE) applied to each bit and to each received symbol.\n",
    "\n",
    "*Remark:* The LLRs computed by the demapper are *logits* on the transmitted bits, and can therefore be used as-is to compute the BCE without any additional processing.\n",
    "*Remark 2:* The BCE is closely related to an achieveable information rate for bit-interleaved coded modulation systems [1,2]\n",
    "\n",
    "[1] Georg Böcherer, \"Principles of Coded Modulation\", [available online](http://www.georg-boecherer.de/bocherer2018principles.pdf)\n",
    "\n",
    "[2] F. Ait Aoudia and J. Hoydis, \"End-to-End Learning for OFDM: From Neural Receivers to Pilotless Communication,\" in IEEE Transactions on Wireless Communications, vol. 21, no. 2, pp. 1049-1063, Feb. 2022, doi: 10.1109/TWC.2021.3101364."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03d7c7b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:43:43.569378Z",
     "iopub.status.busy": "2025-03-08T13:43:43.569081Z",
     "iopub.status.idle": "2025-03-08T13:43:43.659481Z",
     "shell.execute_reply": "2025-03-08T13:43:43.658488Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BCE: 0.00012394244549795985\n"
     ]
    }
   ],
   "source": [
    "bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
    "\n",
    "print(f\"BCE: {bce(bits, llr)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "393dc7d2",
   "metadata": {},
   "source": [
    "One iteration of SGD consists in three steps:\n",
    "1. Perform a forward pass through the end-to-end system and compute the loss function\n",
    "2. Compute the gradient of the loss function with respect to the trainable weights\n",
    "3. Apply the gradient to the weights\n",
    "\n",
    "To enable gradient computation, we need to perform the forward pass (step 1) within a `GradientTape`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a6654b8a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:43:43.663277Z",
     "iopub.status.busy": "2025-03-08T13:43:43.662982Z",
     "iopub.status.idle": "2025-03-08T13:43:43.692937Z",
     "shell.execute_reply": "2025-03-08T13:43:43.692158Z"
    }
   },
   "outputs": [],
   "source": [
    "with tf.GradientTape() as tape:\n",
    "    bits = binary_source([BATCH_SIZE,\n",
    "                            1200]) # Blocklength\n",
    "    mapper.constellation.points = tf.complex(trainable_points[0], trainable_points[1])\n",
    "    x = mapper(bits)\n",
    "    y = awgn_channel(x, no)\n",
    "    llr = demapper(y,no)\n",
    "    loss = bce(bits, llr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12d20342",
   "metadata": {},
   "source": [
    "Using the ``GradientTape``, computing the gradient is done as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a68657af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:43:43.696829Z",
     "iopub.status.busy": "2025-03-08T13:43:43.696515Z",
     "iopub.status.idle": "2025-03-08T13:43:44.014310Z",
     "shell.execute_reply": "2025-03-08T13:43:44.013358Z"
    }
   },
   "outputs": [],
   "source": [
    "gradient = tape.gradient(loss, [trainable_points])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "318aa681",
   "metadata": {},
   "source": [
    "`gradient` is a list of tensors, each tensor corresponding to a trainable variable of our model.\n",
    "\n",
    "For this model, we only have a single trainable tensor: The constellation of shape [`2`, `2^NUM_BITS_PER_SYMBOL`], the first dimension corresponding to the real and imaginary components of the constellation points.\n",
    "\n",
    "*Remark:* It is important to notice that the gradient computation was performed *through the demapper and channel*, which are conventional non-trainable algorithms implemented as *differentiable* Sionna blocks. This key feature of Sionna enables the training of end-to-end communication systems that combine both trainable and conventional and/or non-trainable signal processing algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7437d3f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:43:44.018286Z",
     "iopub.status.busy": "2025-03-08T13:43:44.017992Z",
     "iopub.status.idle": "2025-03-08T13:43:44.022321Z",
     "shell.execute_reply": "2025-03-08T13:43:44.021561Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 64)\n"
     ]
    }
   ],
   "source": [
    "for g in gradient:\n",
    "    print(g.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f7a8cc",
   "metadata": {},
   "source": [
    "Applying the gradient (third step) is performed using an *optimizer*. [Many optimizers are available as part of TensorFlow](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers), and we use in this notebook ``Adam``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "26b73d64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:43:44.026261Z",
     "iopub.status.busy": "2025-03-08T13:43:44.025948Z",
     "iopub.status.idle": "2025-03-08T13:43:44.034252Z",
     "shell.execute_reply": "2025-03-08T13:43:44.033505Z"
    }
   },
   "outputs": [],
   "source": [
    "optimizer = tf.keras.optimizers.Adam(1e-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2b15d3",
   "metadata": {},
   "source": [
    "Using the optimizer, the gradients can be applied to the trainable weights to update them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "54565c6b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:43:44.036450Z",
     "iopub.status.busy": "2025-03-08T13:43:44.036212Z",
     "iopub.status.idle": "2025-03-08T13:43:44.056068Z",
     "shell.execute_reply": "2025-03-08T13:43:44.055292Z"
    }
   },
   "outputs": [],
   "source": [
    "optimizer.apply_gradients(zip(gradient, tape.watched_variables()));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9ef8110",
   "metadata": {},
   "source": [
    "Let's compare the constellation before and after the gradient application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31dd793c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:43:44.058606Z",
     "iopub.status.busy": "2025-03-08T13:43:44.058356Z",
     "iopub.status.idle": "2025-03-08T13:43:44.580531Z",
     "shell.execute_reply": "2025-03-08T13:43:44.579886Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sn.phy.mapping.Constellation(\"qam\", NUM_BITS_PER_SYMBOL).show()\n",
    "fig.axes[0].scatter(trainable_points[0], trainable_points[1], label='After SGD')\n",
    "fig.axes[0].legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a936c10b",
   "metadata": {},
   "source": [
    "The SGD step has led to slight change in the position of the constellation points. Training of a communication system using SGD consists in looping over such SGD steps until a stop criterion is met."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "008a93d5",
   "metadata": {},
   "source": [
    "## Creating Custom Layers<a class=\"anchor\" id=\"Creating-Custom-Layers\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "244c594f",
   "metadata": {},
   "source": [
    "Custom trainable (or not trainable) algorithms can be implemented as [Keras layers](https://keras.io/api/layers/) or Sionna blocks. All Sionna components, such as the mapper, demapper, channel... are implemented as Sionna blocks.\n",
    "\n",
    "To illustrate how this can be done, the next cell implements a simple neural network-based demapper which consists of three dense layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9670872b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:43:44.585001Z",
     "iopub.status.busy": "2025-03-08T13:43:44.584686Z",
     "iopub.status.idle": "2025-03-08T13:43:44.590125Z",
     "shell.execute_reply": "2025-03-08T13:43:44.589585Z"
    }
   },
   "outputs": [],
   "source": [
    "class NeuralDemapper(Layer): # Inherits from Keras Layer\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        # The three dense layers that form the custom trainable neural network-based demapper\n",
    "        self.dense_1 = Dense(64, 'relu')\n",
    "        self.dense_2 = Dense(64, 'relu')\n",
    "        self.dense_3 = Dense(NUM_BITS_PER_SYMBOL, None) # The last layer has no activation and therefore outputs logits, i.e., LLRs\n",
    "\n",
    "    def call(self, y):\n",
    "\n",
    "        # y : complex-valued with shape [batch size, block length]\n",
    "        # y is first mapped to a real-valued tensor with shape\n",
    "        #  [batch size, block length, 2]\n",
    "        # where the last dimension consists of the real and imaginary components\n",
    "        # The dense layers operate on the last dimension, and treat the inner dimensions as batch dimensions, i.e.,\n",
    "        # all the received symbols are independently processed.\n",
    "        nn_input = tf.stack([tf.math.real(y), tf.math.imag(y)], axis=-1)\n",
    "        z = self.dense_1(nn_input)\n",
    "        z = self.dense_2(z)\n",
    "        z = self.dense_3(z) # [batch size, number of symbols per block, number of bits per symbol]\n",
    "        llr = tf.reshape(z, [tf.shape(y)[0], -1]) # [batch size, number of bits per block]\n",
    "        return llr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6552c1a7",
   "metadata": {},
   "source": [
    "A custom Keras layer is used as any other Sionna layer, and therefore integration to a Sionna-based communication is straightforward.\n",
    "\n",
    "The following model uses the neural demapper instead of the conventional demapper. It takes at initialization a parameter that indicates if the model is intantiated to be trained or evaluated. When instantiated to be trained, the loss function is returned. Otherwise, the transmitted bits and LLRs are returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "502066d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:43:44.593682Z",
     "iopub.status.busy": "2025-03-08T13:43:44.593391Z",
     "iopub.status.idle": "2025-03-08T13:43:44.602519Z",
     "shell.execute_reply": "2025-03-08T13:43:44.601805Z"
    }
   },
   "outputs": [],
   "source": [
    "class End2EndSystem(Model): # Inherits from Keras Model\n",
    "\n",
    "    def __init__(self, training):\n",
    "\n",
    "        super().__init__() # Must call the Keras model initializer\n",
    "\n",
    "        qam_constellation = sn.phy.mapping.Constellation(\"qam\", NUM_BITS_PER_SYMBOL) \n",
    "\n",
    "        self.points = tf.Variable(tf.stack([tf.math.real(qam_constellation.points),\n",
    "                                            tf.math.imag(qam_constellation.points)], axis=0))\n",
    "\n",
    "        self.constellation = sn.phy.mapping.Constellation(\"custom\",\n",
    "                                                    num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "                                                    points = tf.complex(self.points[0], self.points[1]),\n",
    "                                                    normalize=True,\n",
    "                                                    center=True)\n",
    "        self.mapper = sn.phy.mapping.Mapper(constellation=self.constellation)\n",
    "        self.demapper = NeuralDemapper() # Intantiate the NeuralDemapper custom layer as any other\n",
    "        self.binary_source = sn.phy.mapping.BinarySource()\n",
    "        self.awgn_channel = sn.phy.channel.AWGN()\n",
    "        self.bce = tf.keras.losses.BinaryCrossentropy(from_logits=True) # Loss function\n",
    "        self.training = training\n",
    "\n",
    "    @tf.function(jit_compile=True) # Enable graph execution to speed things up\n",
    "    def __call__(self, batch_size, ebno_db):\n",
    "\n",
    "        # no channel coding used; we set coderate=1.0\n",
    "        no = sn.phy.utils.ebnodb2no(ebno_db,\n",
    "                                num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "                                coderate=1.0)\n",
    "        bits = self.binary_source([batch_size, 1200]) # Blocklength set to 1200 bits\n",
    "\n",
    "        # Assign points to constellation\n",
    "        self.mapper.constellation.points = tf.complex(self.points[0], self.points[1])\n",
    "\n",
    "        x = self.mapper(bits)\n",
    "        y = self.awgn_channel(x, no)\n",
    "        llr = self.demapper(y)  # Call the NeuralDemapper custom layer as any other\n",
    "        if self.training:\n",
    "            loss = self.bce(bits, llr)\n",
    "            return loss\n",
    "        else:\n",
    "            return bits, llr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af122bb3",
   "metadata": {},
   "source": [
    "When a model that includes a neural network is created, the neural network weights are randomly initialized typically leading to very poor performance.\n",
    "\n",
    "To see this, the following cell benchmarks the previously defined untrained model against a conventional baseline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6eda9275",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:43:44.605118Z",
     "iopub.status.busy": "2025-03-08T13:43:44.604831Z",
     "iopub.status.idle": "2025-03-08T13:44:01.362532Z",
     "shell.execute_reply": "2025-03-08T13:44:01.361862Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     10.0 | 2.5964e-02 | 1.0000e+00 |        3988 |      153600 |          128 |         128 |         0.9 |reached target block errors\n",
      "   10.526 | 2.1296e-02 | 1.0000e+00 |        3271 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   11.053 | 1.6087e-02 | 1.0000e+00 |        2471 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   11.579 | 1.2728e-02 | 1.0000e+00 |        1955 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   12.105 | 8.6003e-03 | 1.0000e+00 |        1321 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   12.632 | 6.6081e-03 | 1.0000e+00 |        1015 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   13.158 | 4.4206e-03 | 9.9219e-01 |         679 |      153600 |          127 |         128 |         0.0 |reached target block errors\n",
      "   13.684 | 2.8906e-03 | 9.6875e-01 |         444 |      153600 |          124 |         128 |         0.0 |reached target block errors\n",
      "   14.211 | 1.7122e-03 | 8.7500e-01 |         263 |      153600 |          112 |         128 |         0.0 |reached target block errors\n",
      "   14.737 | 9.8633e-04 | 6.7578e-01 |         303 |      307200 |          173 |         256 |         0.0 |reached target block errors\n",
      "   15.263 | 4.7201e-04 | 4.0625e-01 |         145 |      307200 |          104 |         256 |         0.0 |reached target block errors\n",
      "   15.789 | 2.8212e-04 | 2.9948e-01 |         130 |      460800 |          115 |         384 |         0.0 |reached target block errors\n",
      "   16.316 | 1.3889e-04 | 1.4583e-01 |         128 |      921600 |          112 |         768 |         0.1 |reached target block errors\n",
      "   16.842 | 6.0369e-05 | 7.1733e-02 |         102 |     1689600 |          101 |        1408 |         0.2 |reached target block errors\n",
      "   17.368 | 2.5801e-05 | 3.0671e-02 |         107 |     4147200 |          106 |        3456 |         0.5 |reached target block errors\n",
      "   17.895 | 7.2888e-06 | 8.7466e-03 |         103 |    14131200 |          103 |       11776 |         1.7 |reached target block errors\n",
      "   18.421 | 2.4740e-06 | 2.9688e-03 |          38 |    15360000 |           38 |       12800 |         1.9 |reached max iterations\n",
      "   18.947 | 5.8594e-07 | 7.0312e-04 |           9 |    15360000 |            9 |       12800 |         1.8 |reached max iterations\n",
      "   19.474 | 1.9531e-07 | 2.3437e-04 |           3 |    15360000 |            3 |       12800 |         1.9 |reached max iterations\n",
      "     20.0 | 0.0000e+00 | 0.0000e+00 |           0 |    15360000 |            0 |       12800 |         1.8 |reached max iterations\n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 20.0 dB.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     10.0 | 5.3581e-01 | 1.0000e+00 |       82300 |      153600 |          128 |         128 |         3.6 |reached target block errors\n",
      "   10.526 | 5.3594e-01 | 1.0000e+00 |       82320 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   11.053 | 5.3661e-01 | 1.0000e+00 |       82424 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   11.579 | 5.3367e-01 | 1.0000e+00 |       81971 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   12.105 | 5.3426e-01 | 1.0000e+00 |       82062 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   12.632 | 5.3704e-01 | 1.0000e+00 |       82489 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   13.158 | 5.3468e-01 | 1.0000e+00 |       82127 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   13.684 | 5.3520e-01 | 1.0000e+00 |       82206 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   14.211 | 5.3441e-01 | 1.0000e+00 |       82085 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   14.737 | 5.3374e-01 | 1.0000e+00 |       81982 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   15.263 | 5.3430e-01 | 1.0000e+00 |       82068 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   15.789 | 5.3494e-01 | 1.0000e+00 |       82167 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   16.316 | 5.3466e-01 | 1.0000e+00 |       82124 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   16.842 | 5.3814e-01 | 1.0000e+00 |       82658 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   17.368 | 5.3357e-01 | 1.0000e+00 |       81956 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   17.895 | 5.3414e-01 | 1.0000e+00 |       82044 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   18.421 | 5.3462e-01 | 1.0000e+00 |       82118 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   18.947 | 5.3362e-01 | 1.0000e+00 |       81964 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   19.474 | 5.3307e-01 | 1.0000e+00 |       81880 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "     20.0 | 5.3419e-01 | 1.0000e+00 |       82052 |      153600 |          128 |         128 |         0.0 |reached target block errors\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "EBN0_DB_MIN = 10.0\n",
    "EBN0_DB_MAX = 20.0\n",
    "\n",
    "\n",
    "###############################\n",
    "# Baseline\n",
    "###############################\n",
    "\n",
    "class Baseline(Model): # Inherits from Keras Model\n",
    "\n",
    "    def __init__(self):\n",
    "\n",
    "        super().__init__() # Must call the Keras model initializer\n",
    "\n",
    "        self.constellation = sn.phy.mapping.Constellation(\"qam\", NUM_BITS_PER_SYMBOL)\n",
    "        self.mapper = sn.phy.mapping.Mapper(constellation=self.constellation)\n",
    "        self.demapper = sn.phy.mapping.Demapper(\"app\", constellation=self.constellation)\n",
    "        self.binary_source = sn.phy.mapping.BinarySource()\n",
    "        self.awgn_channel = sn.phy.channel.AWGN()\n",
    "\n",
    "    @tf.function # Enable graph execution to speed things up\n",
    "    def __call__(self, batch_size, ebno_db):\n",
    "\n",
    "        # no channel coding used; we set coderate=1.0\n",
    "        no = sn.phy.utils.ebnodb2no(ebno_db,\n",
    "                                num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "                                coderate=1.0)\n",
    "        bits = self.binary_source([batch_size, 1200]) # Blocklength set to 1200 bits\n",
    "        x = self.mapper(bits)\n",
    "        y = self.awgn_channel(x, no)\n",
    "        llr = self.demapper(y,no)\n",
    "        return bits, llr\n",
    "\n",
    "###############################\n",
    "# Benchmarking\n",
    "###############################\n",
    "\n",
    "baseline = Baseline()\n",
    "model = End2EndSystem(False)\n",
    "ber_plots = sn.phy.utils.PlotBER(\"Neural Demapper\")\n",
    "ber_plots.simulate(baseline,\n",
    "                  ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 20),\n",
    "                  batch_size=BATCH_SIZE,\n",
    "                  num_target_block_errors=100, # simulate until 100 block errors occured\n",
    "                  legend=\"Baseline\",\n",
    "                  soft_estimates=True,\n",
    "                  max_mc_iter=100, # run 100 Monte-Carlo simulations (each with batch_size samples)\n",
    "                  show_fig=False);\n",
    "ber_plots.simulate(model,\n",
    "                  ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 20),\n",
    "                  batch_size=BATCH_SIZE,\n",
    "                  num_target_block_errors=100, # simulate until 100 block errors occured\n",
    "                  legend=\"Untrained model\",\n",
    "                  soft_estimates=True,\n",
    "                  max_mc_iter=100, # run 100 Monte-Carlo simulations (each with batch_size samples)\n",
    "                  show_fig=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4982feda-3b93-46dc-a936-bd225d7260dd",
   "metadata": {},
   "source": [
    "## Setting up Training Loops <a class=\"anchor\" id=\"Setting-up-Training-Loops\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "257d7e00-7341-4542-bc75-d9a612fba091",
   "metadata": {},
   "source": [
    "Training of end-to-end communication systems consists in iterating over SGD steps.\n",
    "\n",
    "The next cell implements a training loop of `NUM_TRAINING_ITERATIONS` iterations.\n",
    "The training SNR is set to $E_b/N_0 = 15$ dB.\n",
    "\n",
    "At each iteration:\n",
    "- A forward pass through the end-to-end system is performed within a gradient tape\n",
    "- The gradients are computed using the gradient tape, and applied using the Adam optimizer\n",
    "- The estimated loss is periodically printed to follow the progress of training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c53960db-28ee-4f33-8840-7b1373348162",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:44:01.367708Z",
     "iopub.status.busy": "2025-03-08T13:44:01.367500Z",
     "iopub.status.idle": "2025-03-08T13:47:51.479257Z",
     "shell.execute_reply": "2025-03-08T13:47:51.478283Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9900/10000  Loss: 2.19E-03\r"
     ]
    }
   ],
   "source": [
    "# Number of iterations used for training\n",
    "NUM_TRAINING_ITERATIONS = 10000\n",
    "\n",
    "# Instantiating the end-to-end model for training\n",
    "model_train = End2EndSystem(training=True)\n",
    "\n",
    "# Adam optimizer (SGD variant)\n",
    "optimizer = tf.keras.optimizers.Adam()\n",
    "\n",
    "# Training loop\n",
    "for i in range(NUM_TRAINING_ITERATIONS):\n",
    "    # Forward pass\n",
    "    with tf.GradientTape() as tape:\n",
    "        loss = model_train(BATCH_SIZE, 15.0) # The model is assumed to return the BMD rate\n",
    "    # Computing and applying gradients\n",
    "    grads = tape.gradient(loss, model_train.trainable_weights)\n",
    "    optimizer.apply_gradients(zip(grads, model_train.trainable_weights))\n",
    "    # Print progress\n",
    "    if i % 100 == 0:\n",
    "        print(f\"{i}/{NUM_TRAINING_ITERATIONS}  Loss: {loss:.2E}\", end=\"\\r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "185884d2",
   "metadata": {},
   "source": [
    "The weights of the trained model are saved using [pickle](https://docs.python.org/3/library/pickle.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c1eaf2c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:47:51.483276Z",
     "iopub.status.busy": "2025-03-08T13:47:51.483056Z",
     "iopub.status.idle": "2025-03-08T13:47:51.488653Z",
     "shell.execute_reply": "2025-03-08T13:47:51.487809Z"
    }
   },
   "outputs": [],
   "source": [
    "# Save the weightsin a file\n",
    "weights = model_train.get_weights()\n",
    "with open('weights-neural-demapper', 'wb') as f:\n",
    "    pickle.dump(weights, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bc9252a-47ee-4c2b-9224-194c90d7f4f2",
   "metadata": {},
   "source": [
    "Finally, we evaluate the trained model and benchmark it against the previously introduced baseline.\n",
    "\n",
    "We first instantiate the model for evaluation and load the saved weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6a0e6e6b-3650-4524-9c83-46c715493e3f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:47:51.492607Z",
     "iopub.status.busy": "2025-03-08T13:47:51.492296Z",
     "iopub.status.idle": "2025-03-08T13:47:53.887852Z",
     "shell.execute_reply": "2025-03-08T13:47:53.887154Z"
    }
   },
   "outputs": [],
   "source": [
    "# Instantiating the end-to-end model for evaluation\n",
    "model = End2EndSystem(training=False)\n",
    "# Run one inference to build the layers and loading the weights\n",
    "model(tf.constant(1, tf.int32), tf.constant(10.0, tf.float32))\n",
    "with open('weights-neural-demapper', 'rb') as f:\n",
    "    weights = pickle.load(f)\n",
    "    model.set_weights(weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e6372e-d5a4-40ae-90fb-2bb1694dd533",
   "metadata": {},
   "source": [
    "The trained model is then evaluated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fc0e47a5-7d61-4e8b-b077-8fc63a33057f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-08T13:47:53.892052Z",
     "iopub.status.busy": "2025-03-08T13:47:53.891792Z",
     "iopub.status.idle": "2025-03-08T13:48:02.545801Z",
     "shell.execute_reply": "2025-03-08T13:48:02.545145Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     10.0 | 2.6549e-02 | 1.0000e+00 |        4078 |      153600 |          128 |         128 |         1.3 |reached target block errors\n",
      "   10.526 | 2.1003e-02 | 1.0000e+00 |        3226 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   11.053 | 1.6693e-02 | 1.0000e+00 |        2564 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   11.579 | 1.2474e-02 | 1.0000e+00 |        1916 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   12.105 | 9.0039e-03 | 1.0000e+00 |        1383 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   12.632 | 6.6276e-03 | 1.0000e+00 |        1018 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   13.158 | 4.3424e-03 | 1.0000e+00 |         667 |      153600 |          128 |         128 |         0.0 |reached target block errors\n",
      "   13.684 | 2.9102e-03 | 9.6875e-01 |         447 |      153600 |          124 |         128 |         0.0 |reached target block errors\n",
      "   14.211 | 1.7122e-03 | 8.8281e-01 |         263 |      153600 |          113 |         128 |         0.0 |reached target block errors\n",
      "   14.737 | 9.5378e-04 | 7.1094e-01 |         293 |      307200 |          182 |         256 |         0.0 |reached target block errors\n",
      "   15.263 | 5.9570e-04 | 5.0391e-01 |         183 |      307200 |          129 |         256 |         0.0 |reached target block errors\n",
      "   15.789 | 3.2118e-04 | 3.2031e-01 |         148 |      460800 |          123 |         384 |         0.0 |reached target block errors\n",
      "   16.316 | 1.4323e-04 | 1.6094e-01 |         110 |      768000 |          103 |         640 |         0.1 |reached target block errors\n",
      "   16.842 | 6.9661e-05 | 7.9687e-02 |         107 |     1536000 |          102 |        1280 |         0.1 |reached target block errors\n",
      "   17.368 | 2.1159e-05 | 2.4658e-02 |         104 |     4915200 |          101 |        4096 |         0.4 |reached target block errors\n",
      "   17.895 | 8.5663e-06 | 1.0280e-02 |         100 |    11673600 |          100 |        9728 |         0.9 |reached target block errors\n",
      "   18.421 | 2.3437e-06 | 2.8125e-03 |          36 |    15360000 |           36 |       12800 |         1.2 |reached max iterations\n",
      "   18.947 | 3.9063e-07 | 4.6875e-04 |           6 |    15360000 |            6 |       12800 |         1.2 |reached max iterations\n",
      "   19.474 | 1.9531e-07 | 2.3437e-04 |           3 |    15360000 |            3 |       12800 |         1.2 |reached max iterations\n",
      "     20.0 | 6.5104e-08 | 7.8125e-05 |           1 |    15360000 |            1 |       12800 |         1.2 |reached max iterations\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Computing and plotting BER\n",
    "ber_plots.simulate(model,\n",
    "                  ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 20),\n",
    "                  batch_size=BATCH_SIZE,\n",
    "                  num_target_block_errors=100,\n",
    "                  legend=\"Trained model\",\n",
    "                  soft_estimates=True,\n",
    "                  max_mc_iter=100,\n",
    "                  show_fig=True);"
   ]
  }
 ],
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